Open-Meteo API data Analysis (Las Vegas, NV)

# Open-Meteo API data pull

# Notes :
# Location to be analyzed, located at similar Latitude and geographics, but has very different weather (rain precipitation):
#    a. Las Vegas (dry), coord: 36.1716° N, 115.1391° W
#    b. Nashville (wet), coord: 36.1627° N, 86.7816° W
###############################################################################################
import openmeteo_requests
import requests_cache
from retry_requests import retry
import pandas as pd

# Setup the Open-Meteo API client with cache and retry on error
cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
openmeteo = openmeteo_requests.Client(session = retry_session)

# Make sure all required weather variables are listed here
# The order of variables in hourly or daily is important to assign them correctly below
url = "https://api.open-meteo.com/v1/forecast"
params = {
	"latitude": 36.175,
	"longitude": -115.1372,
	"hourly": ["temperature_2m", "relative_humidity_2m", "dew_point_2m", "apparent_temperature", "precipitation_probability", "precipitation", "rain", "showers", "pressure_msl", "surface_pressure", "cloud_cover", "cloud_cover_low", "cloud_cover_mid", "cloud_cover_high", "visibility", "wind_speed_10m", "soil_moisture_0_to_1cm", "soil_moisture_1_to_3cm", "soil_moisture_3_to_9cm", "soil_moisture_9_to_27cm", "soil_moisture_27_to_81cm", "evapotranspiration", "snowfall", "snow_depth", "et0_fao_evapotranspiration", "vapour_pressure_deficit", "wind_speed_80m", "wind_speed_120m", "wind_speed_180m", "wind_direction_10m", "wind_direction_80m", "wind_direction_120m", "wind_direction_180m", "temperature_80m", "temperature_120m", "temperature_180m", "soil_temperature_0cm", "soil_temperature_6cm", "soil_temperature_18cm", "soil_temperature_54cm", "wind_gusts_10m"],
	"past_days": 92,
	"forecast_days": 1
}
responses = openmeteo.weather_api(url, params=params)

# Process first location. Add a for-loop for multiple locations or weather models
response = responses[0]
print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
print(f"Elevation {response.Elevation()} m asl")
print(f"Timezone {response.Timezone()}{response.TimezoneAbbreviation()}")
print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")

# Process hourly data. The order of variables needs to be the same as requested.
hourly = response.Hourly()
hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy()
hourly_dew_point_2m = hourly.Variables(2).ValuesAsNumpy()
hourly_apparent_temperature = hourly.Variables(3).ValuesAsNumpy()
hourly_precipitation_probability = hourly.Variables(4).ValuesAsNumpy()
hourly_precipitation = hourly.Variables(5).ValuesAsNumpy()
hourly_rain = hourly.Variables(6).ValuesAsNumpy()
hourly_showers = hourly.Variables(7).ValuesAsNumpy()
hourly_pressure_msl = hourly.Variables(8).ValuesAsNumpy()
hourly_surface_pressure = hourly.Variables(9).ValuesAsNumpy()
hourly_cloud_cover = hourly.Variables(10).ValuesAsNumpy()
hourly_cloud_cover_low = hourly.Variables(11).ValuesAsNumpy()
hourly_cloud_cover_mid = hourly.Variables(12).ValuesAsNumpy()
hourly_cloud_cover_high = hourly.Variables(13).ValuesAsNumpy()
hourly_visibility = hourly.Variables(14).ValuesAsNumpy()
hourly_wind_speed_10m = hourly.Variables(15).ValuesAsNumpy()
hourly_soil_moisture_0_to_1cm = hourly.Variables(16).ValuesAsNumpy()
hourly_soil_moisture_1_to_3cm = hourly.Variables(17).ValuesAsNumpy()
hourly_soil_moisture_3_to_9cm = hourly.Variables(18).ValuesAsNumpy()
hourly_soil_moisture_9_to_27cm = hourly.Variables(19).ValuesAsNumpy()
hourly_soil_moisture_27_to_81cm = hourly.Variables(20).ValuesAsNumpy()
hourly_evapotranspiration = hourly.Variables(21).ValuesAsNumpy()
hourly_snowfall = hourly.Variables(22).ValuesAsNumpy()
hourly_snow_depth = hourly.Variables(23).ValuesAsNumpy()
hourly_et0_fao_evapotranspiration = hourly.Variables(24).ValuesAsNumpy()
hourly_vapour_pressure_deficit = hourly.Variables(25).ValuesAsNumpy()
hourly_wind_speed_80m = hourly.Variables(26).ValuesAsNumpy()
hourly_wind_speed_120m = hourly.Variables(27).ValuesAsNumpy()
hourly_wind_speed_180m = hourly.Variables(28).ValuesAsNumpy()
hourly_wind_direction_10m = hourly.Variables(29).ValuesAsNumpy()
hourly_wind_direction_80m = hourly.Variables(30).ValuesAsNumpy()
hourly_wind_direction_120m = hourly.Variables(31).ValuesAsNumpy()
hourly_wind_direction_180m = hourly.Variables(32).ValuesAsNumpy()
hourly_temperature_80m = hourly.Variables(33).ValuesAsNumpy()
hourly_temperature_120m = hourly.Variables(34).ValuesAsNumpy()
hourly_temperature_180m = hourly.Variables(35).ValuesAsNumpy()
hourly_soil_temperature_0cm = hourly.Variables(36).ValuesAsNumpy()
hourly_soil_temperature_6cm = hourly.Variables(37).ValuesAsNumpy()
hourly_soil_temperature_18cm = hourly.Variables(38).ValuesAsNumpy()
hourly_soil_temperature_54cm = hourly.Variables(39).ValuesAsNumpy()
hourly_wind_gusts_10m = hourly.Variables(40).ValuesAsNumpy()

hourly_data = {"date": pd.date_range(
	start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
	end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
	freq = pd.Timedelta(seconds = hourly.Interval()),
	inclusive = "left"
)}

hourly_data["temperature_2m"] = hourly_temperature_2m
hourly_data["relative_humidity_2m"] = hourly_relative_humidity_2m
hourly_data["dew_point_2m"] = hourly_dew_point_2m
hourly_data["apparent_temperature"] = hourly_apparent_temperature
hourly_data["precipitation_probability"] = hourly_precipitation_probability
hourly_data["precipitation"] = hourly_precipitation
hourly_data["rain"] = hourly_rain
hourly_data["showers"] = hourly_showers
hourly_data["pressure_msl"] = hourly_pressure_msl
hourly_data["surface_pressure"] = hourly_surface_pressure
hourly_data["cloud_cover"] = hourly_cloud_cover
hourly_data["cloud_cover_low"] = hourly_cloud_cover_low
hourly_data["cloud_cover_mid"] = hourly_cloud_cover_mid
hourly_data["cloud_cover_high"] = hourly_cloud_cover_high
hourly_data["visibility"] = hourly_visibility
hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
hourly_data["soil_moisture_0_to_1cm"] = hourly_soil_moisture_0_to_1cm
hourly_data["soil_moisture_1_to_3cm"] = hourly_soil_moisture_1_to_3cm
hourly_data["soil_moisture_3_to_9cm"] = hourly_soil_moisture_3_to_9cm
hourly_data["soil_moisture_9_to_27cm"] = hourly_soil_moisture_9_to_27cm
hourly_data["soil_moisture_27_to_81cm"] = hourly_soil_moisture_27_to_81cm
hourly_data["evapotranspiration"] = hourly_evapotranspiration
hourly_data["snowfall"] = hourly_snowfall
hourly_data["snow_depth"] = hourly_snow_depth
hourly_data["et0_fao_evapotranspiration"] = hourly_et0_fao_evapotranspiration
hourly_data["vapour_pressure_deficit"] = hourly_vapour_pressure_deficit
hourly_data["wind_speed_80m"] = hourly_wind_speed_80m
hourly_data["wind_speed_120m"] = hourly_wind_speed_120m
hourly_data["wind_speed_180m"] = hourly_wind_speed_180m
hourly_data["wind_direction_10m"] = hourly_wind_direction_10m
hourly_data["wind_direction_80m"] = hourly_wind_direction_80m
hourly_data["wind_direction_120m"] = hourly_wind_direction_120m
hourly_data["wind_direction_180m"] = hourly_wind_direction_180m
hourly_data["temperature_80m"] = hourly_temperature_80m
hourly_data["temperature_120m"] = hourly_temperature_120m
hourly_data["temperature_180m"] = hourly_temperature_180m
hourly_data["soil_temperature_0cm"] = hourly_soil_temperature_0cm
hourly_data["soil_temperature_6cm"] = hourly_soil_temperature_6cm
hourly_data["soil_temperature_18cm"] = hourly_soil_temperature_18cm
hourly_data["soil_temperature_54cm"] = hourly_soil_temperature_54cm
hourly_data["wind_gusts_10m"] = hourly_wind_gusts_10m

hourly_dataframe = pd.DataFrame(data = hourly_data)
print(hourly_dataframe)

Coordinates 36.16437911987305°N -115.1439208984375°E
Elevation 609.0 m asl
Timezone NoneNone
Timezone difference to GMT+0 0 s
                          date  temperature_2m  relative_humidity_2m  \
0    2025-03-01 00:00:00+00:00             NaN                   NaN   
1    2025-03-01 01:00:00+00:00             NaN                   NaN   
2    2025-03-01 02:00:00+00:00             NaN                   NaN   
3    2025-03-01 03:00:00+00:00             NaN                   NaN   
4    2025-03-01 04:00:00+00:00             NaN                   NaN   
...                        ...             ...                   ...   
2227 2025-06-01 19:00:00+00:00       39.169502                  11.0   
2228 2025-06-01 20:00:00+00:00       39.219501                  11.0   
2229 2025-06-01 21:00:00+00:00       39.269501                  12.0   
2230 2025-06-01 22:00:00+00:00       39.219501                  12.0   
2231 2025-06-01 23:00:00+00:00       38.669502                  12.0   

      dew_point_2m  apparent_temperature  precipitation_probability  \
0              NaN                   NaN                        0.0   
1              NaN                   NaN                        0.0   
2              NaN                   NaN                        0.0   
3              NaN                   NaN                        0.0   
4              NaN                   NaN                        0.0   
...            ...                   ...                        ...   
2227      3.341017             36.008011                        1.0   
2228      3.379128             35.056602                        1.0   
2229      4.657262             36.113987                        2.0   
2230      4.618772             35.940289                        2.0   
2231      4.195249             33.902687                        3.0   

      precipitation  rain  showers  pressure_msl  surface_pressure  \
0               NaN   NaN      NaN           NaN               NaN   
1               NaN   NaN      NaN           NaN               NaN   
2               NaN   NaN      NaN           NaN               NaN   
3               NaN   NaN      NaN           NaN               NaN   
4               NaN   NaN      NaN           NaN               NaN   
...             ...   ...      ...           ...               ...   
2227            0.0   0.0      0.0   1005.900024        941.469604   
2228            0.0   0.0      0.0   1005.200012        940.824158   
2229            0.0   0.0      0.0   1004.700012        940.366394   
2230            0.0   0.0      0.0   1003.700012        939.420227   
2231            0.0   0.0      0.0   1003.599976        939.217957   

      cloud_cover  cloud_cover_low  cloud_cover_mid  cloud_cover_high  \
0             NaN              NaN              NaN               NaN   
1             NaN              NaN              NaN               NaN   
2             NaN              NaN              NaN               NaN   
3             NaN              NaN              NaN               NaN   
4             NaN              NaN              NaN               NaN   
...           ...              ...              ...               ...   
2227        100.0              0.0              1.0             100.0   
2228        100.0              0.0              0.0             100.0   
2229        100.0              0.0              5.0             100.0   
2230         55.0              0.0              0.0              55.0   
2231         76.0              0.0              0.0              76.0   

      visibility  wind_speed_10m  soil_moisture_0_to_1cm  \
0            NaN             NaN                     NaN   
1            NaN             NaN                     NaN   
2            NaN             NaN                     NaN   
3            NaN             NaN                     NaN   
4            NaN             NaN                     NaN   
...          ...             ...                     ...   
2227     90000.0       23.950148                   0.032   
2228     90000.0       29.686359                   0.032   
2229     90000.0       25.499081                   0.031   
2230     90000.0       24.485292                   0.031   
2231     90000.0       29.810522                   0.031   

      soil_moisture_1_to_3cm  soil_moisture_3_to_9cm  soil_moisture_9_to_27cm  \
0                        NaN                     NaN                      NaN   
1                        NaN                     NaN                      NaN   
2                        NaN                     NaN                      NaN   
3                        NaN                     NaN                      NaN   
4                        NaN                     NaN                      NaN   
...                      ...                     ...                      ...   
2227                   0.076                   0.105                    0.103   
2228                   0.076                   0.105                    0.103   
2229                   0.076                   0.105                    0.102   
2230                   0.076                   0.105                    0.102   
2231                   0.076                   0.105                    0.102   

      soil_moisture_27_to_81cm  evapotranspiration  snowfall  snow_depth  \
0                          NaN                 0.0       NaN         NaN   
1                          NaN                 0.0       NaN         NaN   
2                          NaN                 0.0       NaN         NaN   
3                          NaN                 0.0       NaN         NaN   
4                          NaN                 0.0       NaN         NaN   
...                        ...                 ...       ...         ...   
2227                      0.11                -0.0       0.0         0.0   
2228                      0.11                -0.0       0.0         0.0   
2229                      0.11                -0.0       0.0         0.0   
2230                      0.11                -0.0       0.0         0.0   
2231                      0.11                -0.0       0.0         0.0   

      et0_fao_evapotranspiration  vapour_pressure_deficit  wind_speed_80m  \
0                            NaN                      NaN             NaN   
1                            NaN                      NaN             NaN   
2                            NaN                      NaN             NaN   
3                            NaN                      NaN             NaN   
4                            NaN                      NaN             NaN   
...                          ...                      ...             ...   
2227                    0.989649                 6.279035       26.729847   
2228                    1.043472                 6.295875       33.092594   
2229                    1.006306                 6.241590       28.585449   
2230                    0.951399                 6.224906       27.267473   
2231                    0.912528                 6.043872       35.188046   

      wind_speed_120m  wind_speed_180m  wind_direction_10m  \
0                 NaN              NaN                 NaN   
1                 NaN              NaN                 NaN   
2                 NaN              NaN                 NaN   
3                 NaN              NaN                 NaN   
4                 NaN              NaN                 NaN   
...               ...              ...                 ...   
2227        42.661774        31.433586          222.563309   
2228        36.379044        31.926540          194.036270   
2229        46.478695        30.993471          210.547501   
2230        49.953789        34.131718          221.423569   
2231        48.185936        33.985218          227.447083   

      wind_direction_80m  wind_direction_120m  wind_direction_180m  \
0                    NaN                  NaN                  NaN   
1                    NaN                  NaN                  NaN   
2                    NaN                  NaN                  NaN   
3                    NaN                  NaN                  NaN   
4                    NaN                  NaN                  NaN   
...                  ...                  ...                  ...   
2227          224.454407           169.077240           159.904678   
2228          194.489807           179.421295           187.124924   
2229          211.087433           235.304779           210.735413   
2230          222.324509           222.018463           217.716049   
2231          228.317871           182.622345           216.384445   

      temperature_80m  temperature_120m  temperature_180m  \
0                 NaN               NaN               NaN   
1                 NaN               NaN               NaN   
2                 NaN               NaN               NaN   
3                 NaN               NaN               NaN   
4                 NaN               NaN               NaN   
...               ...               ...               ...   
2227        34.609997         34.359997         32.563999   
2228        35.059998         34.809998         32.963997   
2229        34.809998         34.559998         32.863998   
2230        34.059998         33.809998         32.764000   
2231        32.859997         32.609997         32.764000   

      soil_temperature_0cm  soil_temperature_6cm  soil_temperature_18cm  \
0                      NaN                   NaN                    NaN   
1                      NaN                   NaN                    NaN   
2                      NaN                   NaN                    NaN   
3                      NaN                   NaN                    NaN   
4                      NaN                   NaN                    NaN   
...                    ...                   ...                    ...   
2227             45.113998             40.563999              33.863998   
2228             44.113998             40.563999              34.663998   
2229             44.363998             40.813999              35.363998   
2230             42.963997             40.813999              35.913998   
2231             42.413998             40.363998              36.313999   

      soil_temperature_54cm  wind_gusts_10m  
0                       NaN             NaN  
1                       NaN             NaN  
2                       NaN             NaN  
3                       NaN             NaN  
4                       NaN             NaN  
...                     ...             ...  
2227              30.714001       27.359999  
2228              30.764000       33.480000  
2229              30.764000       29.160000  
2230              30.814001       28.080000  
2231              30.864000       36.360001  

[2232 rows x 42 columns]

# Store the data into the CSV file
path1 = r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\Global_Weather\openmeteo_data_lasvegas_nv.csv'
hourly_dataframe.to_csv(path1, header='column_names')

# Visualize geolocation by Latitude and Longitude of Las Vegas and Nashville
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20,10)

# Load a world map shapefile (built-in dataset)
world = gpd.read_file(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_admin_0_countries.shp')
worldpop = gpd.read_file(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_populated_places.shp')

#    a. Las Vegas (dry), coord: 36.1716° N, 115.1391° W
#    b. Nashville (wet), coord: 36.1627° N, 86.7816° W

data = {'longitude': [-115.1391, -86.7816],
        'latitude': [36.1716, 36.1627],
        'value': [10, 20]}

df = pd.DataFrame(data)

# Create geometry column
geometry = gpd.points_from_xy(df['longitude'], df['latitude'], crs="EPSG:4326")

# Create GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry=geometry)

print(gdf)

usapop = worldpop[worldpop['ADM0NAME'] == 'United States of America']

# # Plotting multiple layers
fig, ax = plt.subplots()
plt.title('Geolocation of Las Vegas and Nashville')
world.plot(ax = ax, cmap = 'hsv', edgecolor = 'black', column = 'SOVEREIGNT')
gdf.plot(ax = ax, color = 'yellow', markersize = 40)  # The location of Las Vegas and Nashville can be seen as yellow dots in the map

   longitude  latitude  value                   geometry
0  -115.1391   36.1716     10  POINT (-115.1391 36.1716)
1   -86.7816   36.1627     20   POINT (-86.7816 36.1627)

<Axes: title={'center': 'Geolocation of Las Vegas and Nashville'}>

# Visualize Las Vegas (and Nevada) and Nashville (and Tennessee)
# States are depicted with blue color
# Cities are depicted with yellow color
stateprov = gpd.read_file(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_admin_1_states_provinces.shp')

usamain = stateprov[stateprov["name"] != "Alaska"]
usamain = usamain[usamain["name"] != "Hawaii"]
nevada = stateprov[stateprov["name"] == "Nevada"]
tennessee = stateprov[stateprov["name"] == "Tennessee"]

nevada['coords'] = nevada['geometry'].apply(lambda x: x.representative_point().coords[:])
nevada['coords'] = [coords[0] for coords in nevada['coords']]
tennessee['coords'] = tennessee['geometry'].apply(lambda x: x.representative_point().coords[:])
tennessee['coords'] = [coords[0] for coords in tennessee['coords']]

# # Plotting multiple layers
fig, ax = plt.subplots()

for idx, row in nevada.iterrows():
    plt.annotate(text=row['name'], xy=row['coords'],
                 horizontalalignment='center')

for idx, row in tennessee.iterrows():
    plt.annotate(text=row['name'], xy=row['coords'],
                 horizontalalignment='center')

usamain.plot(ax = ax, color='green', edgecolor = 'black')
nevada.plot(ax = ax, color = 'blue', markersize = 40)
tennessee.plot(ax = ax, color = 'blue', markersize = 40)
gdf.plot(ax = ax, color = 'yellow', markersize = 40)

C:\Users\Paulus Hendarwan\AppData\Roaming\Python\Python312\site-packages\geopandas\geodataframe.py:1981: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
C:\Users\Paulus Hendarwan\AppData\Roaming\Python\Python312\site-packages\geopandas\geodataframe.py:1981: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

<Axes: >

# Maps of Las Vegas
import pandas as pd
import geopandas as gpd
gdf = gpd.read_file(r"C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\cities.geojson")
lasvegasdf = gdf.loc[gdf['NAME'].isin(['LAS VEGAS'])]

# Remove all unwanted Las Vegases, just leave the one with Index 19199, Las Vegas in Nevada state.
# We use drop Method coz this Method doesn't change the GeoDataframe into Series.
# If we use .iloc or .loc Method, it will change the GeoDataframe into Series.
lasvegasdf = lasvegasdf.drop(19212)
lasvegasdf = lasvegasdf.drop(24676)

lasvegasdf.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

# Open-Meteo Data Analysis

# Read from the CSV file
import pandas as pd
import matplotlib.pyplot as plt
path1 = r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\Global_Weather\openmeteo_data_lasvegas_nv.csv'
df1 = pd.read_csv(path1)
df1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	...	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m
0	0	2025-03-01 00:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1	2025-03-01 01:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2	2025-03-01 02:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	3	2025-03-01 03:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	4	2025-03-01 04:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	2227	2025-06-01 19:00:00+00:00	39.169502	11.0	3.341017	36.008010	1.0	0.0	0.0	0.0	...	169.07724	159.90468	34.609997	34.359997	32.564000	45.114000	40.564	33.864000	30.714000	27.359999
2228	2228	2025-06-01 20:00:00+00:00	39.219500	11.0	3.379128	35.056602	1.0	0.0	0.0	0.0	...	179.42130	187.12492	35.059998	34.809998	32.963997	44.114000	40.564	34.663998	30.764000	33.480000
2229	2229	2025-06-01 21:00:00+00:00	39.269500	12.0	4.657262	36.113987	2.0	0.0	0.0	0.0	...	235.30478	210.73541	34.809998	34.559998	32.864000	44.364000	40.814	35.364000	30.764000	29.160000
2230	2230	2025-06-01 22:00:00+00:00	39.219500	12.0	4.618772	35.940290	2.0	0.0	0.0	0.0	...	222.01846	217.71605	34.059998	33.809998	32.764000	42.963997	40.814	35.913998	30.814001	28.080000
2231	2231	2025-06-01 23:00:00+00:00	38.669502	12.0	4.195249	33.902687	3.0	0.0	0.0	0.0	...	182.62234	216.38445	32.859997	32.609997	32.764000	42.413998	40.364	36.314000	30.864000	36.360000

2232 rows × 43 columns

# Data Cleaning

# Remove Duplicate data from raw table
df1 = df1.drop_duplicates()

# Drop/remove all entries with blanks
# df.dropna(subset='temperature_2m', inplace=True)
# df.dropna(subset='relative_humidity_2m', inplace=True)
df1.dropna(inplace=True)

# Set to display all columns
pd.set_option('display.max_columns',None)
plt.rcParams['figure.figsize'] = (12, 8)

# Check whether there's still any blank rows/columns
df1[df1.isna().any(axis=1)]

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m

# Add new Columns to assist in analysis

# Add Column 'Location'
df1['Location'] = 'Las Vegas'

# Split the Date into Year, Month and Date
df1[['ymd','hmstz']] = df1['date'].str.split(' ', expand=True)
df1[['year','month','day']] = df1['ymd'].str.split('-', expand=True)
df1[['hour','minute','sectz','tzsec']] = df1['hmstz'].str.split(':', expand=True)

df1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
437	437	2025-03-19 05:00:00+00:00	14.459500	14.0	-12.757421	8.626808	0.0	0.0	0.0	0.0	1024.6	953.57040	0.0	0.0	0.0	0.0	24140.0	16.343367	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.151726	1.417936	22.768398	24.519993	29.555100	352.405430	18.435053	19.249622	55.922764	12.460000	12.360001	11.264000	9.714001	12.814000	16.114000	15.314000	22.680000	Las Vegas	2025-03-19	05:00:00+00:00	2025	03	19	05	00	00+00	00
438	438	2025-03-19 06:00:00+00:00	13.459500	17.0	-11.145491	8.152900	0.0	0.0	0.0	0.0	1025.4	954.07745	0.0	0.0	0.0	0.0	24140.0	13.493999	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.120954	1.282614	23.565567	26.150720	30.500309	43.919170	18.711840	19.708078	59.500250	12.010000	11.960000	10.364000	9.264000	12.414001	15.714001	15.364000	24.840000	Las Vegas	2025-03-19	06:00:00+00:00	2025	03	19	06	00	00+00	00
439	439	2025-03-19 07:00:00+00:00	12.659500	20.0	-9.752880	7.904532	0.0	0.0	0.0	0.0	1026.2	954.63040	0.0	0.0	0.0	0.0	24140.0	10.440000	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.090544	1.173374	21.900904	23.996807	23.277834	46.397110	25.301346	25.387870	58.276447	11.460000	11.410000	9.464001	8.164001	11.614000	15.264000	15.364000	19.080000	Las Vegas	2025-03-19	07:00:00+00:00	2025	03	19	07	00	00+00	00
440	440	2025-03-19 08:00:00+00:00	12.159500	21.0	-9.549829	7.835932	0.0	0.0	0.0	0.0	1026.8	955.06824	0.0	0.0	0.0	0.0	24140.0	7.594208	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.065000	1.121307	17.873556	19.561127	15.629971	58.570484	27.597204	28.009092	61.073680	10.760000	10.760000	8.864000	6.914000	10.764000	14.764000	15.364000	12.959999	Las Vegas	2025-03-19	08:00:00+00:00	2025	03	19	08	00	00+00	00
441	441	2025-03-19 09:00:00+00:00	11.559500	22.0	-9.461175	7.294112	0.0	0.0	0.0	0.0	1027.4	955.48145	0.0	0.0	0.0	0.0	24140.0	7.244860	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.059745	1.064184	17.114204	19.654087	8.913181	63.435013	22.249071	20.806877	46.636536	10.460000	10.460000	8.564000	5.864000	9.864000	14.264000	15.364000	11.520000	Las Vegas	2025-03-19	09:00:00+00:00	2025	03	19	09	00	00+00	00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	2227	2025-06-01 19:00:00+00:00	39.169502	11.0	3.341017	36.008010	1.0	0.0	0.0	0.0	1005.9	941.46960	100.0	0.0	1.0	100.0	90000.0	23.950148	0.032	0.076	0.105	0.103	0.110	-0.0	0.0	0.0	0.989649	6.279035	26.729847	42.661774	31.433586	222.563310	224.454400	169.077240	159.904680	34.609997	34.359997	32.564000	45.114000	40.564000	33.864000	30.714000	27.359999	Las Vegas	2025-06-01	19:00:00+00:00	2025	06	01	19	00	00+00	00
2228	2228	2025-06-01 20:00:00+00:00	39.219500	11.0	3.379128	35.056602	1.0	0.0	0.0	0.0	1005.2	940.82416	100.0	0.0	0.0	100.0	90000.0	29.686360	0.032	0.076	0.105	0.103	0.110	-0.0	0.0	0.0	1.043472	6.295875	33.092594	36.379044	31.926540	194.036270	194.489800	179.421300	187.124920	35.059998	34.809998	32.963997	44.114000	40.564000	34.663998	30.764000	33.480000	Las Vegas	2025-06-01	20:00:00+00:00	2025	06	01	20	00	00+00	00
2229	2229	2025-06-01 21:00:00+00:00	39.269500	12.0	4.657262	36.113987	2.0	0.0	0.0	0.0	1004.7	940.36640	100.0	0.0	5.0	100.0	90000.0	25.499080	0.031	0.076	0.105	0.102	0.110	-0.0	0.0	0.0	1.006306	6.241590	28.585450	46.478695	30.993471	210.547500	211.087430	235.304780	210.735410	34.809998	34.559998	32.864000	44.364000	40.814000	35.364000	30.764000	29.160000	Las Vegas	2025-06-01	21:00:00+00:00	2025	06	01	21	00	00+00	00
2230	2230	2025-06-01 22:00:00+00:00	39.219500	12.0	4.618772	35.940290	2.0	0.0	0.0	0.0	1003.7	939.42020	55.0	0.0	0.0	55.0	90000.0	24.485292	0.031	0.076	0.105	0.102	0.110	-0.0	0.0	0.0	0.951399	6.224906	27.267473	49.953790	34.131718	221.423570	222.324510	222.018460	217.716050	34.059998	33.809998	32.764000	42.963997	40.814000	35.913998	30.814001	28.080000	Las Vegas	2025-06-01	22:00:00+00:00	2025	06	01	22	00	00+00	00
2231	2231	2025-06-01 23:00:00+00:00	38.669502	12.0	4.195249	33.902687	3.0	0.0	0.0	0.0	1003.6	939.21796	76.0	0.0	0.0	76.0	90000.0	29.810522	0.031	0.076	0.105	0.102	0.110	-0.0	0.0	0.0	0.912528	6.043872	35.188046	48.185936	33.985218	227.447080	228.317870	182.622340	216.384450	32.859997	32.609997	32.764000	42.413998	40.364000	36.314000	30.864000	36.360000	Las Vegas	2025-06-01	23:00:00+00:00	2025	06	01	23	00	00+00	00

1795 rows × 53 columns

# Data Analysis

# Find out the correlation among Columns to filter what Columns we'll use for analysis
# For first glance, we filter out by common logic, what Columns are possible to have strong enough correlations.
# We choose a bunch of Columns specified with Index numbers.
df1h = df1[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m',
            'dew_point_2m','vapour_pressure_deficit','temperature_2m','cloud_cover_high','cloud_cover_low','wind_speed_10m',
            'soil_moisture_3_to_9cm','temperature_80m','soil_temperature_0cm']]
df1h

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
437	0.0	0.0	0.038	0.067	14.0	-12.757421	1.417936	14.459500	0.0	0.0	16.343367	0.100	12.460000	9.714001
438	0.0	0.0	0.038	0.067	17.0	-11.145491	1.282614	13.459500	0.0	0.0	13.493999	0.100	12.010000	9.264000
439	0.0	0.0	0.038	0.067	20.0	-9.752880	1.173374	12.659500	0.0	0.0	10.440000	0.100	11.460000	8.164001
440	0.0	0.0	0.038	0.067	21.0	-9.549829	1.121307	12.159500	0.0	0.0	7.594208	0.100	10.760000	6.914000
441	0.0	0.0	0.038	0.067	22.0	-9.461175	1.064184	11.559500	0.0	0.0	7.244860	0.100	10.460000	5.864000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	1.0	1.0	0.032	0.076	11.0	3.341017	6.279035	39.169502	100.0	0.0	23.950148	0.105	34.609997	45.114000
2228	1.0	0.0	0.032	0.076	11.0	3.379128	6.295875	39.219500	100.0	0.0	29.686360	0.105	35.059998	44.114000
2229	2.0	5.0	0.031	0.076	12.0	4.657262	6.241590	39.269500	100.0	0.0	25.499080	0.105	34.809998	44.364000
2230	2.0	0.0	0.031	0.076	12.0	4.618772	6.224906	39.219500	55.0	0.0	24.485292	0.105	34.059998	42.963997
2231	3.0	0.0	0.031	0.076	12.0	4.195249	6.043872	38.669502	76.0	0.0	29.810522	0.105	32.859997	42.413998

1795 rows × 14 columns

# Create a Function to generate sorted, limited Correlation Table of a certain Column/parameter
def gencorr(param):
    df1hcorr = df1h.corr(numeric_only=True).abs().sort_values(by=param, ascending=False)
    df1hcorr = df1hcorr.where(df1hcorr[param] > 0.5)
    df1hcorr = df1hcorr.dropna()
    corresult = df1hcorr[param].round(2)
    return corresult

# Generate Correlation Table for 'temperature_2m'
gencorr('temperature_2m')

temperature_2m             1.00
vapour_pressure_deficit    0.96
temperature_80m            0.93
soil_temperature_0cm       0.84
relative_humidity_2m       0.57
Name: temperature_2m, dtype: float64

# Draw plot of the most correlated column (vapour_pressure_deficit) with 'temperature_2m' with dual y-axis plot.
# The 'temperature_2m' and 'vapour_pressure_deficit' fluctuations for the most part are similar except at several points.
df04 = df1.where(df1['month'] == '04').dropna()
df05 = df1.where(df1['month'] == '05').dropna()

import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('temperature_2m vs vapour_pressure_deficit in April 2025')
ax1.plot(df04[['temperature_2m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['vapour_pressure_deficit']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('temperature_2m vs vapour_pressure_deficit in May 2025')
ax1.plot(df05[['temperature_2m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['vapour_pressure_deficit']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c817958ce0>]

# Draw plot of the most correlated columns (soil_temperature_0cm) with 'temperature_2m' in single y-axis plot.
# For the most part of the Plot, the fluctuation of the 'soil_temperature_0cm' is similar except on several points.
df1[['temperature_2m','soil_temperature_0cm']].plot(subplots=[('temperature_2m','soil_temperature_0cm')])

array([<Axes: >], dtype=object)

# Generate Correlation Table for 'relative_humidity_2m'
gencorr('relative_humidity_2m')

relative_humidity_2m         1.00
dew_point_2m                 0.73
soil_moisture_0_to_1cm       0.68
vapour_pressure_deficit      0.66
cloud_cover_low              0.64
temperature_80m              0.59
temperature_2m               0.57
precipitation_probability    0.56
Name: relative_humidity_2m, dtype: float64

# Draw plot of the most correlated column (dew_point_2m) with 'relative_humidity_2m'
# The relative_humidity_2m values fluctuations for the most part are similar to dew_point_2m except at several points.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('relative_humidity_2m vs dew_point_2m in April 2025')
ax1.plot(df04[['relative_humidity_2m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['dew_point_2m']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('relative_humidity_2m vs dew_point_2m in May 2025')
ax1.plot(df05[['relative_humidity_2m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['dew_point_2m']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c817d9a390>]

# Draw plot of the most correlated column (soil_moisture_0_to_1cm) with 'relative_humidity_2m'
# The relative_humidity_2m values fluctuations for the most part are similar to dew_point_2m except at several points.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('relative_humidity_2m vs soil_moisture_0_to_1cm in April 2025')
ax1.plot(df04[['relative_humidity_2m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['soil_moisture_0_to_1cm']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('relative_humidity_2m vs soil_moisture_0_to_1cm in May 2025')
ax1.plot(df05[['relative_humidity_2m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['soil_moisture_0_to_1cm']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c818798c80>]

# Generate Correlation Table for 'dew_point_2m'
gencorr('dew_point_2m')

dew_point_2m              1.00
relative_humidity_2m      0.73
soil_moisture_0_to_1cm    0.55
soil_moisture_1_to_3cm    0.51
Name: dew_point_2m, dtype: float64

# Draw plot of the most correlated column (soil_moisture_0_to_1cm) with 'dew_point_2m'
# The dew_point_2m values fluctuations for the more than halfpart are similar to soil_moisture_0_to_1cm.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('dew_point_2m vs soil_moisture_0_to_1cm in April 2025')
ax1.plot(df04[['dew_point_2m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['soil_moisture_0_to_1cm']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('dew_point_2m vs soil_moisture_0_to_1cm in May 2025')
ax1.plot(df05[['dew_point_2m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['soil_moisture_0_to_1cm']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c81a2fd370>]

# Generate Correlation Table for 'precipitation_probability'
gencorr('precipitation_probability')

precipitation_probability    1.00
cloud_cover_low              0.61
relative_humidity_2m         0.56
Name: precipitation_probability, dtype: float64

# Draw plot of the most correlated column (cloud_cover_low) with 'precipitation_probability'
# The dew_point_2m values fluctuations for the more than halfpart are similar to soil_moisture_0_to_1cm.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('precipitation_probability vs cloud_cover_low in April 2025')
ax1.plot(df04[['precipitation_probability']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['cloud_cover_low']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('precipitation_probability vs cloud_cover_low in May 2025')
ax1.plot(df05[['precipitation_probability']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['cloud_cover_low']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c81a8d55e0>]

# Generate Correlation Table for 'cloud_cover_low'
gencorr('cloud_cover_low')

cloud_cover_low              1.00
relative_humidity_2m         0.64
precipitation_probability    0.61
soil_moisture_0_to_1cm       0.50
Name: cloud_cover_low, dtype: float64

# Draw plot of the most correlated column (relative_humidity_2m) with 'cloud_cover_low'
# The 'cloud_cover_low' and 'relative_humidity_2m' fluctuations for more than half part are similar except at several points.
import matplotlib.pyplot as plt
df04[['cloud_cover_low','relative_humidity_2m']].plot(title='cloud_cover_low vs relative_humidity_2m in April 2025')
df05[['cloud_cover_low','relative_humidity_2m']].plot(title='cloud_cover_low vs relative_humidity_2m in May 2025')

<Axes: title={'center': 'cloud_cover_low vs relative_humidity_2m in May 2025'}>

# Generate Correlation Table for 'cloud_cover_mid'
gencorr('cloud_cover_mid')

cloud_cover_mid    1.0
Name: cloud_cover_mid, dtype: float64

# Generate Correlation Table for 'cloud_cover_high'
gencorr('cloud_cover_high')

cloud_cover_high    1.0
Name: cloud_cover_high, dtype: float64

# Generate Correlation Table for 'wind_speed_10m'
gencorr('wind_speed_10m')

wind_speed_10m    1.0
Name: wind_speed_10m, dtype: float64

# Generate Correlation Table for 'soil_moisture_0_to_1cm'
gencorr('soil_moisture_0_to_1cm')

soil_moisture_0_to_1cm    1.00
soil_moisture_1_to_3cm    0.71
relative_humidity_2m      0.68
soil_moisture_3_to_9cm    0.60
dew_point_2m              0.55
cloud_cover_low           0.50
Name: soil_moisture_0_to_1cm, dtype: float64

# Draw plot of the most correlated column (soil_moisture_1_to_3cm) with 'soil_moisture_0_to_1cm'
# The 'soil_moisture_0_to_1cm' and 'soil_moisture_1_to_3cm' fluctuations for more than half part are similar.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_moisture_0_to_1cm','soil_moisture_1_to_3cm']].plot(title='soil_moisture_0_to_1cm vs soil_moisture_1_to_3cm in April 2025')
df05[['soil_moisture_0_to_1cm','soil_moisture_1_to_3cm']].plot(title='soil_moisture_0_to_1cm vs soil_moisture_1_to_3cm in May 2025')

<Axes: title={'center': 'soil_moisture_0_to_1cm vs soil_moisture_1_to_3cm in May 2025'}>

# Draw plot of the most correlated column (soil_moisture_3_to_9cm) with 'soil_moisture_0_to_1cm'
# The 'soil_moisture_0_to_1cm' and 'soil_moisture_3_to_9cm' fluctuations for more than half part are similar.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_moisture_0_to_1cm','soil_moisture_3_to_9cm']].plot(title='soil_moisture_0_to_1cm vs soil_moisture_3_to_9cm in April 2025')
df05[['soil_moisture_0_to_1cm','soil_moisture_3_to_9cm']].plot(title='soil_moisture_0_to_1cm vs soil_moisture_3_to_9cm in May 2025')

<Axes: title={'center': 'soil_moisture_0_to_1cm vs soil_moisture_3_to_9cm in May 2025'}>

# Generate Correlation Table for 'temperature_80m'
gencorr('temperature_80m')

temperature_80m            1.00
temperature_2m             0.93
vapour_pressure_deficit    0.90
soil_temperature_0cm       0.68
relative_humidity_2m       0.59
Name: temperature_80m, dtype: float64

# Draw plot of the most correlated column with 'temperature_80m'
# The 'temperature_80m' and 'temperature_2m' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['temperature_80m','temperature_2m']].plot(title='temperature_80m vs temperature_2m in April 2025')
df05[['temperature_80m','temperature_2m']].plot(title='temperature_80m vs temperature_2m in May 2025')

<Axes: title={'center': 'temperature_80m vs temperature_2m in May 2025'}>

# Draw plot of the most correlated column (cloud_cover_low) with 'temperature_80m'
# The vapour_pressure_deficit values fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('temperature_80m vs vapour_pressure_deficit in April 2025')
ax1.plot(df04[['temperature_80m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['vapour_pressure_deficit']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('temperature_80m vs vapour_pressure_deficit in May 2025')
ax1.plot(df05[['temperature_80m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['vapour_pressure_deficit']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c818823470>]

# Draw plot of the most correlated column with 'temperature_80m'
# The 'temperature_80m' and 'soil_temperature_0cm' fluctuations for the most part are similar except at several points.
import matplotlib.pyplot as plt
# plt.title('temperature_80m vs soil_temperature_0cm in April 2025')
df04[['temperature_80m','soil_temperature_0cm']].plot(title='temperature_80m vs soil_temperature_0cm in April 2025')
df05[['temperature_80m','soil_temperature_0cm']].plot(title='temperature_80m vs soil_temperature_0cm in May 2025')

<Axes: title={'center': 'temperature_80m vs soil_temperature_0cm in May 2025'}>

# Generate Correlation Table for 'soil_temperature_0cm'
gencorr('soil_temperature_0cm')

soil_temperature_0cm       1.00
temperature_2m             0.84
vapour_pressure_deficit    0.80
temperature_80m            0.68
Name: soil_temperature_0cm, dtype: float64

# Draw plot of the most correlated column (vapour_pressure_deficit) with 'soil_temperature_0cm'
# The soil_temperature_0cm and vapour_pressure_deficit values fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('soil_temperature_0cm vs vapour_pressure_deficit in April 2025')
ax1.plot(df04[['soil_temperature_0cm']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['vapour_pressure_deficit']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('soil_temperature_0cm vs vapour_pressure_deficit in May 2025')
ax1.plot(df05[['soil_temperature_0cm']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['vapour_pressure_deficit']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c81bd86ff0>]

# Generate Correlation Table for 'soil_moisture_1_to_3cm'
gencorr('soil_moisture_1_to_3cm')

soil_moisture_1_to_3cm    1.00
soil_moisture_3_to_9cm    0.95
soil_moisture_0_to_1cm    0.71
dew_point_2m              0.51
Name: soil_moisture_1_to_3cm, dtype: float64

# Draw plot of the most correlated column with 'soil_moisture_1_to_3cm'
# The 'soil_moisture_1_to_3cm' and 'soil_moisture_3_to_9cm' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_moisture_1_to_3cm','soil_moisture_3_to_9cm']].plot(title='soil_moisture_1_to_3cm vs soil_moisture_3_to_9cm in April 2025')
df05[['soil_moisture_1_to_3cm','soil_moisture_3_to_9cm']].plot(title='soil_moisture_1_to_3cm vs soil_moisture_3_to_9cm in May 2025')

<Axes: title={'center': 'soil_moisture_1_to_3cm vs soil_moisture_3_to_9cm in May 2025'}>

# Generate Correlation Table for 'soil_moisture_3_to_9cm'
gencorr('soil_moisture_3_to_9cm')

soil_moisture_3_to_9cm    1.00
soil_moisture_1_to_3cm    0.95
soil_moisture_0_to_1cm    0.60
Name: soil_moisture_3_to_9cm, dtype: float64

# Generate Correlation Table for 'vapour_pressure_deficit'
gencorr('vapour_pressure_deficit')

vapour_pressure_deficit    1.00
temperature_2m             0.96
temperature_80m            0.90
soil_temperature_0cm       0.80
relative_humidity_2m       0.66
Name: vapour_pressure_deficit, dtype: float64

# Draw plot of the most correlated column (vapour_pressure_deficit) with 'relative_humidity_2m'
# The vapour_pressure_deficit and relative_humidity_2m values fluctuations for more than half part are good reverse.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('vapour_pressure_deficit vs relative_humidity_2m in April 2025')
ax1.plot(df04[['vapour_pressure_deficit']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['relative_humidity_2m']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('vapour_pressure_deficit vs relative_humidity_2m in May 2025')
ax1.plot(df05[['vapour_pressure_deficit']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['relative_humidity_2m']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x1c81dd623f0>]

# Draw the Heatmap of Weather Data Correlation
import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(df1h.corr(numeric_only=True), annot=True)
plt.rcParams['figure.figsize'] = (20,10)
plt.show()

# How about if we limit the 'temperature_2m' to certain range, let's say, more than 18C
# Data for 4 Columns where temperature_2m more than 18 C
df1h2 = df1h.where(df1h['temperature_2m'] > 18)
df1h2 = df1h2.dropna()
df1h2

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
479	0.0	0.0	0.037	0.067	12.0	-11.587272	1.859420	18.359500	100.0	0.0	4.104631	0.099	17.410000	26.514000
480	0.0	0.0	0.038	0.069	12.0	-11.116996	1.930476	18.959500	100.0	0.0	3.563818	0.101	17.960001	23.164000
481	0.0	0.0	0.038	0.069	11.0	-11.971617	1.989208	19.259500	100.0	0.0	2.545584	0.101	18.110000	19.364000
482	0.0	0.0	0.038	0.069	11.0	-11.894015	2.001618	19.359500	93.0	0.0	4.349896	0.101	18.010000	15.264000
483	0.0	0.0	0.038	0.069	11.0	-11.971617	1.989208	19.259500	46.0	0.0	6.120000	0.101	17.810000	13.264000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	1.0	1.0	0.032	0.076	11.0	3.341017	6.279035	39.169502	100.0	0.0	23.950148	0.105	34.609997	45.114000
2228	1.0	0.0	0.032	0.076	11.0	3.379128	6.295875	39.219500	100.0	0.0	29.686360	0.105	35.059998	44.114000
2229	2.0	5.0	0.031	0.076	12.0	4.657262	6.241590	39.269500	100.0	0.0	25.499080	0.105	34.809998	44.364000
2230	2.0	0.0	0.031	0.076	12.0	4.618772	6.224906	39.219500	55.0	0.0	24.485292	0.105	34.059998	42.963997
2231	3.0	0.0	0.031	0.076	12.0	4.195249	6.043872	38.669502	76.0	0.0	29.810522	0.105	32.859997	42.413998

1329 rows × 14 columns

sns.heatmap(df1h2.corr(numeric_only=True), annot=True)
plt.rcParams['figure.figsize'] = (20,10)
plt.show()

######### Conclusion from the Heatmap ##############
# 'relative_humidity_2m' has correlation of -0.86 to 'vapour_pressure_deficit'.
# 'temperature_80m' has correlation of 0.92 to 'soil_temperature_6cm'.
# 'temperature_80m' has correlation of 0.86 to 'soil_temperature_0cm'.
# 'temperature_2m' has correlation of 0.86 to 'soil_temperature_0cm'.
# 'dew_point_2m' has correlation of 0.81 to 'relative_humidity_2m' parameter if temperature_2m > 18C
# 'cloud_cover_mid' has correlation of 0.51 to 'precipitation_probability'.

# LAB : Check 'corr' method of Pandas with mathematical high order polynomial equations (quadratic to power-of-5 equations)
# Equations used:
# 1. y1 = 2x^2 + 5x + 6
# 2. y2 = 2x^3 + 7x^2 + 10x + 20
# 3. y3 = 5x^4 + 12x^3 + 8x^2 + 6x + 20
# 4. y4 = 3x^5 + 8x^4 + 10x^3 + 4x^2 + 9x + 17
dfq = pd.read_csv(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\Global_Weather\quadratic_equation.csv')
dfq

	x	y1	y2	y3	y4
0	1	13	39	51	51
1	2	24	84	240	355
2	3	39	167	839	1727
3	4	58	300	2220	5877
4	5	81	495	4875	15787
5	6	108	764	9416	36071
6	7	139	1119	16575	73335
7	8	174	1572	27204	136537
8	9	213	2135	42275	237347
9	10	256	2820	62880	390507
10	11	303	3639	90231	614191
11	12	354	4604	125660	930365
12	13	409	5727	170619	1365147
13	14	468	7020	226680	1949167
14	15	531	8495	295535	2717927
15	16	598	10164	378996	3712161
16	17	669	12039	478995	4978195
17	18	744	14132	597584	6568307

sns.heatmap(dfq.corr(numeric_only=True), annot=True)
plt.rcParams['figure.figsize'] = (20,10)
plt.show()

# CONCLUSION: 
# 'corr' Method of Pandas is accurate enough until cube equation, but for higher order equation, is much less.
# Correlation value of 0.8 is still can be regarded as correlated enough, due to corr method algorithm limitation.

# The plot of all data of all Columns/parameters
df1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
437	437	2025-03-19 05:00:00+00:00	14.459500	14.0	-12.757421	8.626808	0.0	0.0	0.0	0.0	1024.6	953.57040	0.0	0.0	0.0	0.0	24140.0	16.343367	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.151726	1.417936	22.768398	24.519993	29.555100	352.405430	18.435053	19.249622	55.922764	12.460000	12.360001	11.264000	9.714001	12.814000	16.114000	15.314000	22.680000	Las Vegas	2025-03-19	05:00:00+00:00	2025	03	19	05	00	00+00	00
438	438	2025-03-19 06:00:00+00:00	13.459500	17.0	-11.145491	8.152900	0.0	0.0	0.0	0.0	1025.4	954.07745	0.0	0.0	0.0	0.0	24140.0	13.493999	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.120954	1.282614	23.565567	26.150720	30.500309	43.919170	18.711840	19.708078	59.500250	12.010000	11.960000	10.364000	9.264000	12.414001	15.714001	15.364000	24.840000	Las Vegas	2025-03-19	06:00:00+00:00	2025	03	19	06	00	00+00	00
439	439	2025-03-19 07:00:00+00:00	12.659500	20.0	-9.752880	7.904532	0.0	0.0	0.0	0.0	1026.2	954.63040	0.0	0.0	0.0	0.0	24140.0	10.440000	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.090544	1.173374	21.900904	23.996807	23.277834	46.397110	25.301346	25.387870	58.276447	11.460000	11.410000	9.464001	8.164001	11.614000	15.264000	15.364000	19.080000	Las Vegas	2025-03-19	07:00:00+00:00	2025	03	19	07	00	00+00	00
440	440	2025-03-19 08:00:00+00:00	12.159500	21.0	-9.549829	7.835932	0.0	0.0	0.0	0.0	1026.8	955.06824	0.0	0.0	0.0	0.0	24140.0	7.594208	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.065000	1.121307	17.873556	19.561127	15.629971	58.570484	27.597204	28.009092	61.073680	10.760000	10.760000	8.864000	6.914000	10.764000	14.764000	15.364000	12.959999	Las Vegas	2025-03-19	08:00:00+00:00	2025	03	19	08	00	00+00	00
441	441	2025-03-19 09:00:00+00:00	11.559500	22.0	-9.461175	7.294112	0.0	0.0	0.0	0.0	1027.4	955.48145	0.0	0.0	0.0	0.0	24140.0	7.244860	0.038	0.067	0.100	0.113	0.113	0.0	0.0	0.0	0.059745	1.064184	17.114204	19.654087	8.913181	63.435013	22.249071	20.806877	46.636536	10.460000	10.460000	8.564000	5.864000	9.864000	14.264000	15.364000	11.520000	Las Vegas	2025-03-19	09:00:00+00:00	2025	03	19	09	00	00+00	00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	2227	2025-06-01 19:00:00+00:00	39.169502	11.0	3.341017	36.008010	1.0	0.0	0.0	0.0	1005.9	941.46960	100.0	0.0	1.0	100.0	90000.0	23.950148	0.032	0.076	0.105	0.103	0.110	-0.0	0.0	0.0	0.989649	6.279035	26.729847	42.661774	31.433586	222.563310	224.454400	169.077240	159.904680	34.609997	34.359997	32.564000	45.114000	40.564000	33.864000	30.714000	27.359999	Las Vegas	2025-06-01	19:00:00+00:00	2025	06	01	19	00	00+00	00
2228	2228	2025-06-01 20:00:00+00:00	39.219500	11.0	3.379128	35.056602	1.0	0.0	0.0	0.0	1005.2	940.82416	100.0	0.0	0.0	100.0	90000.0	29.686360	0.032	0.076	0.105	0.103	0.110	-0.0	0.0	0.0	1.043472	6.295875	33.092594	36.379044	31.926540	194.036270	194.489800	179.421300	187.124920	35.059998	34.809998	32.963997	44.114000	40.564000	34.663998	30.764000	33.480000	Las Vegas	2025-06-01	20:00:00+00:00	2025	06	01	20	00	00+00	00
2229	2229	2025-06-01 21:00:00+00:00	39.269500	12.0	4.657262	36.113987	2.0	0.0	0.0	0.0	1004.7	940.36640	100.0	0.0	5.0	100.0	90000.0	25.499080	0.031	0.076	0.105	0.102	0.110	-0.0	0.0	0.0	1.006306	6.241590	28.585450	46.478695	30.993471	210.547500	211.087430	235.304780	210.735410	34.809998	34.559998	32.864000	44.364000	40.814000	35.364000	30.764000	29.160000	Las Vegas	2025-06-01	21:00:00+00:00	2025	06	01	21	00	00+00	00
2230	2230	2025-06-01 22:00:00+00:00	39.219500	12.0	4.618772	35.940290	2.0	0.0	0.0	0.0	1003.7	939.42020	55.0	0.0	0.0	55.0	90000.0	24.485292	0.031	0.076	0.105	0.102	0.110	-0.0	0.0	0.0	0.951399	6.224906	27.267473	49.953790	34.131718	221.423570	222.324510	222.018460	217.716050	34.059998	33.809998	32.764000	42.963997	40.814000	35.913998	30.814001	28.080000	Las Vegas	2025-06-01	22:00:00+00:00	2025	06	01	22	00	00+00	00
2231	2231	2025-06-01 23:00:00+00:00	38.669502	12.0	4.195249	33.902687	3.0	0.0	0.0	0.0	1003.6	939.21796	76.0	0.0	0.0	76.0	90000.0	29.810522	0.031	0.076	0.105	0.102	0.110	-0.0	0.0	0.0	0.912528	6.043872	35.188046	48.185936	33.985218	227.447080	228.317870	182.622340	216.384450	32.859997	32.609997	32.764000	42.413998	40.364000	36.314000	30.864000	36.360000	Las Vegas	2025-06-01	23:00:00+00:00	2025	06	01	23	00	00+00	00

1795 rows × 53 columns

# Try to find the other Columns value when the 'temperature_2m' is maximum
#temperature_2m_max = df[df1.columns[1]].max()  # OR temperature_2m_max = df['temperature_2m'].max()
temperature_2m_max = df1['temperature_2m'].max()
print(temperature_2m_max)
df1ht2mmax = df1.where(df1['temperature_2m'] == temperature_2m_max)
df1ht2mmax = df1ht2mmax.dropna()
df1ht2mmax[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

41.3195

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
2206	2025-05-31 22:00:00+00:00	41.3195	7.0	-1.346894	0.0	0.0	46.0	0.0	5.116561	0.031	0.077	0.106	7.356871	37.809998	50.064

# Try to find the other Columns value when the 'temperature_2m' is minimum
temperature_2m_min = df1['temperature_2m'].min()
print(temperature_2m_min)
df1ht2mmin = df1.where(df1['temperature_2m'] == temperature_2m_min)
df1ht2mmin = df1ht2mmin.dropna()
df1ht2mmin[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

6.4195

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
805	2025-04-03 13:00:00+00:00	6.4195	62.0	-0.337105	0.0	38.0	42.0	0.0	4.379589	0.073	0.041	0.093	0.366641	7.41	6.114

# Find values when the 'precipitation_probability' is Maximum
precipitation_probability_max = df1['precipitation_probability'].max()
print(precipitation_probability_max)
df1hppmax = df1.where(df1['precipitation_probability'] == precipitation_probability_max).sort_values(by = ['cloud_cover_mid','soil_moisture_0_to_1cm'], ascending=[False,False])
df1hppmax = df1hppmax.dropna()
df1hppmax[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm',
           'cloud_cover_low','cloud_cover_high','wind_speed_10m','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

70.0

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	cloud_cover_low	cloud_cover_high	wind_speed_10m	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
1583	2025-05-05 23:00:00+00:00	15.3695	93.0	14.243151	70.0	80.0	0.264	75.0	60.0	12.682018	0.135	0.137	0.122335	14.21	17.564001

# Find values when the 'precipitation_probability' is 50%
df1hpp50 = df1.where(df1['precipitation_probability'] == 50).sort_values(by = ['cloud_cover_mid'], ascending=[False])
df1hpp50 = df1hpp50.dropna()
df1hpp50[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_mid','cloud_cover_low',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_mid	cloud_cover_low	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm

# Find values when the 'precipitation_probability' is Minimum
precipitation_probability_min = df1['precipitation_probability'].min()
print(precipitation_probability_min)
df1hppmin = df1.where(df1['precipitation_probability'] == precipitation_probability_min).sort_values(by = ['cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[True,True,True,True,True,True,True,True]).head(5)
df1hppmin = df1hppmin.dropna()
df1hppmin[['date','temperature_2m','precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm',
           'soil_moisture_3_to_9cm','relative_humidity_2m','cloud_cover_high','dew_point_2m','vapour_pressure_deficit',
           'cloud_cover_low','wind_speed_10m','soil_moisture_1_to_3cm','temperature_80m','soil_temperature_0cm']]

0.0

	date	temperature_2m	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_3_to_9cm	relative_humidity_2m	cloud_cover_high	dew_point_2m	vapour_pressure_deficit	cloud_cover_low	wind_speed_10m	soil_moisture_1_to_3cm	temperature_80m	soil_temperature_0cm
468	2025-03-20 12:00:00+00:00	12.3595	0.0	-1.0	0.038	0.100	14.0	100.0	-14.438404	1.236642	0.0	6.109403	0.067	11.910000	5.614
1009	2025-04-12 01:00:00+00:00	34.8695	0.0	0.0	0.030	0.088	6.0	0.0	-8.026972	5.248589	0.0	8.311245	0.043	33.359997	33.064
1010	2025-04-12 02:00:00+00:00	32.5195	0.0	0.0	0.030	0.088	7.0	0.0	-7.733895	4.554785	0.0	17.709658	0.043	32.110000	28.664
1006	2025-04-11 22:00:00+00:00	35.0195	0.0	0.0	0.030	0.088	7.0	0.0	-5.913356	5.235899	0.0	18.875126	0.043	31.960001	42.764
1007	2025-04-11 23:00:00+00:00	35.3695	0.0	0.0	0.030	0.088	7.0	0.0	-5.658859	5.337968	0.0	15.077082	0.043	32.710000	40.264

# Try to find the other Columns value when the 'relative_humidity_2m' is maximum
relative_humidity_2m_max = df1['relative_humidity_2m'].max()
print(relative_humidity_2m_max)
df1hrh2mmax = df1.where(df1['relative_humidity_2m'] == relative_humidity_2m_max)
df1hrh2mmax = df1hrh2mmax.dropna()
df1hrh2mmax[['date','relative_humidity_2m','temperature_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

96.0

	date	relative_humidity_2m	temperature_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
1597	2025-05-06 13:00:00+00:00	96.0	14.3695	13.739599	4.0	100.0	34.0	0.0	3.219938	0.206	0.204	0.158	0.065552	13.51	14.064

# Unique Fact: when the 'relative_humidity_2m' reach Maximum value, the 'precipitation_probability' reach almost zero percent (4%).
# This contradicts common logic, which suggests high humidity increases precipitation chances.

# Find values when the 'relative_humidity_2m' is 50%
df1hrh50 = df1.where(df1['relative_humidity_2m'] == 50).sort_values(by = ['precipitation_probability'], ascending=[False])
df1hrh50 = df1hrh50.dropna()
df1hrh50[['date','relative_humidity_2m','temperature_2m','vapour_pressure_deficit','dew_point_2m','precipitation_probability','cloud_cover_mid','cloud_cover_low',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','temperature_80m','soil_temperature_0cm']]

	date	relative_humidity_2m	temperature_2m	vapour_pressure_deficit	dew_point_2m	precipitation_probability	cloud_cover_mid	cloud_cover_low	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
788	2025-04-02 20:00:00+00:00	50.0	11.819500	0.694066	1.725359	15.0	100.0	13.0	0.0	23.977188	0.096	0.058	0.094	9.860001	25.864000
1612	2025-05-07 04:00:00+00:00	50.0	16.469501	0.937701	6.011217	10.0	99.0	0.0	0.0	21.933426	0.232	0.178	0.174	14.960000	15.864000
783	2025-04-02 15:00:00+00:00	50.0	8.869500	0.570095	-0.996837	6.0	100.0	100.0	0.0	29.810522	0.043	0.058	0.094	8.810000	12.464001
1604	2025-05-06 20:00:00+00:00	50.0	20.019500	1.171222	9.279046	6.0	100.0	100.0	0.0	8.647496	0.158	0.186	0.161	19.110000	25.214000
1603	2025-05-06 19:00:00+00:00	50.0	21.669500	1.296059	10.796674	5.0	13.0	20.0	0.0	8.145870	0.171	0.189	0.161	18.010000	24.714000
1641	2025-05-08 09:00:00+00:00	50.0	17.769500	1.017986	7.208303	0.0	0.0	0.0	0.0	7.091177	0.129	0.180	0.191	22.660000	17.414000

# Conclusion: when 'relative_humidity_2m' reaches 50%, the highest 'precipitation_probability' is 15%.
# This is less than half the 'precipitation_probability' in Las Vegas.

# Try to find the other Columns value when the 'relative_humidity_2m' is minimum
relative_humidity_2m_min = df1['relative_humidity_2m'].min()
print(relative_humidity_2m_min)
df1hrh2mmin = df1.where(df1['relative_humidity_2m'] == relative_humidity_2m_min)
df1hrh2mmin = df1hrh2mmin.dropna()
df1hrh2mmin[['date','relative_humidity_2m','temperature_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']].sort_values(by='precipitation_probability', ascending=True)

4.0

	date	relative_humidity_2m	temperature_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
1990	2025-05-22 22:00:00+00:00	4.0	37.869500	-11.091969	0.0	0.0	0.0	0.0	26.525429	0.033	0.088	0.115	6.316258	34.059998	43.814000
1991	2025-05-22 23:00:00+00:00	4.0	38.069500	-10.955633	0.0	0.0	0.0	0.0	25.864943	0.033	0.088	0.115	6.384925	34.109997	41.514000
1992	2025-05-23 00:00:00+00:00	4.0	37.519500	-11.330648	0.0	0.0	0.0	0.0	27.059933	0.033	0.088	0.115	6.197625	34.410000	38.963997
2015	2025-05-23 23:00:00+00:00	4.0	36.619500	-11.944863	0.0	0.0	0.0	0.0	25.772078	0.033	0.086	0.113	5.901392	33.010000	41.064000
2017	2025-05-24 01:00:00+00:00	4.0	35.419502	-12.764890	0.0	0.0	0.0	0.0	25.704100	0.033	0.086	0.113	5.525542	32.960000	35.413998
2018	2025-05-24 02:00:00+00:00	4.0	34.469500	-13.414946	0.0	0.0	0.0	0.0	21.129883	0.033	0.086	0.113	5.242886	32.960000	31.914000

# Conclusion: 
# - when the 'relative_humidity_2m' reaches minimum (4%), the 'precipitation_probability' reaches minimum also (0%).
# - when the 'relative_humidity_2m' reaches minimum (4%), the 'dew_point_2m' reaches very low also (-13.4C) (lower than Nashville, -3.68C).

# Find Daily Average of Precipitation Probability (Table)
dfda1 = df1.groupby('ymd')[df1h.columns[0:]].mean().sort_values('precipitation_probability', ascending=False).head(5)
dfda1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
ymd
2025-05-04	25.833333	90.166667	0.068042	0.067083	46.125000	7.227499	1.409673	20.388250	84.416667	41.875000	13.252176	0.104875	19.235000	23.645250
2025-05-06	16.041667	46.166667	0.198167	0.192750	80.708333	12.630033	0.416490	16.373667	4.083333	77.791667	5.971521	0.154250	15.266250	18.634834
2025-05-05	15.083333	87.833333	0.088417	0.091833	61.750000	9.809922	0.780684	17.438250	43.166667	47.416667	7.254426	0.136167	15.976667	20.030667
2025-04-02	6.541667	69.541667	0.050708	0.058167	34.041667	-4.608082	1.001607	12.475750	0.000000	13.583333	20.729977	0.093875	11.568334	16.072334
2025-04-03	3.166667	56.916667	0.073500	0.040042	41.166667	-1.681834	0.864802	11.790333	0.000000	19.958333	8.770723	0.093000	9.916250	15.389000

# Find Daily Average of relative_humidity_2m (Table)
dfda1 = df1.groupby('ymd')[df1h.columns[[4,0]]].mean().sort_values('relative_humidity_2m', ascending=False).head(5)
dfda1

	relative_humidity_2m	precipitation_probability
ymd
2025-05-06	80.708333	16.041667
2025-05-05	61.750000	15.083333
2025-05-07	47.625000	2.750000
2025-05-04	46.125000	25.833333
2025-04-03	41.166667	3.166667

# Find Daily Average of Precipitation Probability (Plot)
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20, 10)  # Set global size for all plots
dfda2 = df1.groupby('ymd')[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].mean().sort_values('ymd', ascending=True)
dfda2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='ymd'>, <Axes: xlabel='ymd'>], dtype=object)

# Find Daily Average of Relative Humidity 2m (Table)
dfda3 = df1.groupby('ymd')[df1h.columns[[4,0]]].mean().sort_values('relative_humidity_2m', ascending=False).head(10)
dfda3

	relative_humidity_2m	precipitation_probability
ymd
2025-05-06	80.708333	16.041667
2025-05-05	61.750000	15.083333
2025-05-07	47.625000	2.750000
2025-05-04	46.125000	25.833333
2025-04-03	41.166667	3.166667
2025-05-08	35.125000	0.000000
2025-04-02	34.041667	6.541667
2025-05-18	33.125000	2.708333
2025-04-27	31.916667	0.708333
2025-03-31	30.166667	0.000000

# Find Monthly Average values of Precipitation Probability (Table)
dfma1 = df1.groupby(['year','month'])[df1h.columns[0:]].mean().sort_values(by = ['precipitation_probability','cloud_cover_mid',
                                        'soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
                                        'relative_humidity_2m','cloud_cover_high','dew_point_2m','vapour_pressure_deficit'], 
                                        ascending=[False,False,False,False,False,False,False,False,False,False])
dfma1

		precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
year	month
2025	05	2.291667	19.543011	0.055297	0.104656	21.094086	-1.432394	3.013983	26.219500	11.091398	5.444892	12.789477	0.129192	25.585807	28.517293
	06	0.708333	49.541667	0.031708	0.077000	10.916667	0.225036	5.343179	35.834084	64.125000	0.000000	17.594226	0.106000	33.482916	36.422333
	04	0.644444	16.631944	0.035333	0.046068	17.704167	-6.591822	2.215045	20.750542	11.543056	1.559722	13.329188	0.088725	20.237639	23.047889
	03	0.110749	16.781759	0.034495	0.064528	19.198697	-4.984482	1.991165	19.878979	34.921824	0.032573	11.694789	0.097678	19.683290	20.777681

# Find Monthly Average values of Precipitation Probability (Plot)
dfma2 = df1.groupby(['year','month'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].mean().sort_values(by = ['year', 'month'], ascending=[True,True])
# dfma2.plot(subplots=[('precipitation_probability', 'soil_moisture_0_to_1cm')])
dfma2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='year,month'>, <Axes: xlabel='year,month'>],
      dtype=object)

# Find Monthly Average values of Relative Humidity 2m (Table)
dfma3 = df1.groupby(['year','month'])[df1h.columns[[4,0]]].mean().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfma3

		relative_humidity_2m	precipitation_probability
year	month
2025	05	21.094086	2.291667
	03	19.198697	0.110749
	04	17.704167	0.644444
	06	10.916667	0.708333

# Find Hourly Average values of Precipitation Probability (Table)
dfha1 = df1.groupby(['hmstz'])[df1h.columns[0:]].mean().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfha1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
hmstz
23:00:00+00:00	2.106667	24.253333	0.044000	0.073373	13.706667	-4.714941	3.636448	28.481367	22.386667	2.720000	16.456923	0.106573	25.761333	34.534000
22:00:00+00:00	1.920000	21.093333	0.042773	0.073333	14.213333	-4.141996	3.557681	28.212700	21.266667	2.946667	15.882570	0.106680	25.510667	36.917999
00:00:00+00:00	1.662162	22.432432	0.042459	0.073824	13.270270	-4.899519	3.613658	28.393149	15.648649	2.229730	15.621791	0.107784	25.958649	31.574810
21:00:00+00:00	1.586667	19.106667	0.042973	0.073427	14.866667	-3.568545	3.451940	27.828034	19.640000	4.266667	16.420983	0.106840	25.036667	38.148666
19:00:00+00:00	1.466667	16.306667	0.043933	0.073627	16.560000	-2.963268	3.088773	26.308034	15.986667	2.640000	13.408874	0.107000	23.536667	37.547332
06:00:00+00:00	1.413333	19.693333	0.043480	0.074360	18.973333	-4.692392	2.273213	21.802034	18.626667	1.480000	11.827843	0.107427	22.652000	19.536000
05:00:00+00:00	1.413333	17.186667	0.043453	0.074373	18.146667	-4.985937	2.407996	22.539367	17.093333	2.040000	12.258795	0.107413	23.248667	19.642667
20:00:00+00:00	1.413333	16.506667	0.043480	0.073507	15.346667	-3.434113	3.303648	27.206034	16.466667	4.920000	15.200141	0.106933	24.416000	38.527999
16:00:00+00:00	1.400000	11.626667	0.045493	0.073733	23.000000	-1.966810	2.220290	22.006700	11.786667	4.493333	11.209813	0.107187	20.135334	27.214667
01:00:00+00:00	1.364865	22.135135	0.041703	0.073824	13.040541	-5.624604	3.526115	27.933014	14.027027	3.040541	15.342573	0.107676	25.972162	28.274811
17:00:00+00:00	1.253333	12.826667	0.045400	0.073640	20.746667	-2.113576	2.504790	23.600700	12.226667	4.546667	12.077289	0.107067	21.408667	31.439333
15:00:00+00:00	1.186667	15.160000	0.044147	0.073853	25.333333	-2.089175	1.907255	20.053367	14.413333	5.333333	10.679532	0.107227	18.944000	21.740667
04:00:00+00:00	1.148649	19.527027	0.043095	0.073892	16.472973	-5.860641	2.637508	23.703284	17.229730	1.418919	12.263542	0.107581	24.139730	20.843054
18:00:00+00:00	1.146667	15.560000	0.044920	0.073693	18.640000	-2.310659	2.796328	25.056700	12.040000	1.960000	13.473858	0.107133	22.498667	35.103333
02:00:00+00:00	1.108108	20.959459	0.042000	0.073865	13.594595	-6.073569	3.278218	26.678284	13.000000	2.594595	14.039328	0.107649	25.663378	24.609270
07:00:00+00:00	1.106667	18.240000	0.043573	0.074533	20.893333	-4.227496	2.083288	20.730700	20.053333	1.226667	11.549302	0.107413	22.136667	18.688667
14:00:00+00:00	1.093333	18.653333	0.043573	0.073960	26.840000	-2.922083	1.634909	17.983367	14.360000	4.600000	10.348632	0.107240	18.409334	16.935334
03:00:00+00:00	1.067568	18.702703	0.041608	0.073892	14.743243	-6.412960	2.891001	24.889771	16.189189	1.405405	12.525557	0.107608	24.941081	22.227514
13:00:00+00:00	1.040000	16.733333	0.043413	0.074093	26.906667	-3.631291	1.525281	17.000034	15.266667	4.266667	10.705742	0.107293	18.731334	15.197334
08:00:00+00:00	1.013333	20.546667	0.043373	0.074467	21.693333	-4.419502	1.980057	20.010034	17.120000	0.480000	12.706578	0.107333	21.585334	17.969334
12:00:00+00:00	0.920000	16.946667	0.043400	0.074200	25.133333	-4.083975	1.616041	17.612700	14.466667	1.906667	11.465712	0.107333	19.197333	15.551334
10:00:00+00:00	0.693333	18.960000	0.043240	0.074200	23.333333	-4.477055	1.762027	18.589367	15.733333	3.440000	10.635036	0.107320	20.378667	16.764667
11:00:00+00:00	0.640000	19.173333	0.043200	0.074133	24.120000	-4.359386	1.691183	18.083367	13.786667	2.240000	11.644047	0.107227	19.796667	16.214667
09:00:00+00:00	0.520000	17.133333	0.043213	0.074333	22.666667	-4.233425	1.848442	19.193367	16.506667	3.066667	11.518742	0.107333	21.007334	17.353334

# Find Hourly Average values of Precipitation Probability (Plot)
dfha2 = df1.groupby(['hmstz'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].mean().sort_values(by = ['hmstz'], ascending=[True])
dfha2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='hmstz'>, <Axes: xlabel='hmstz'>], dtype=object)

# Find Hourly Average values of Relative Humidity 2m (Table)
dfha3 = df1.groupby(['hmstz'])[df1h.columns[[4,0]]].mean().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfha3

	relative_humidity_2m	precipitation_probability
hmstz
13:00:00+00:00	26.906667	1.040000
14:00:00+00:00	26.840000	1.093333
15:00:00+00:00	25.333333	1.186667
12:00:00+00:00	25.133333	0.920000
11:00:00+00:00	24.120000	0.640000
10:00:00+00:00	23.333333	0.693333
16:00:00+00:00	23.000000	1.400000
09:00:00+00:00	22.666667	0.520000
08:00:00+00:00	21.693333	1.013333
07:00:00+00:00	20.893333	1.106667
17:00:00+00:00	20.746667	1.253333
06:00:00+00:00	18.973333	1.413333
18:00:00+00:00	18.640000	1.146667
05:00:00+00:00	18.146667	1.413333
19:00:00+00:00	16.560000	1.466667
04:00:00+00:00	16.472973	1.148649
20:00:00+00:00	15.346667	1.413333
21:00:00+00:00	14.866667	1.586667
03:00:00+00:00	14.743243	1.067568
22:00:00+00:00	14.213333	1.920000
23:00:00+00:00	13.706667	2.106667
02:00:00+00:00	13.594595	1.108108
00:00:00+00:00	13.270270	1.662162
01:00:00+00:00	13.040541	1.364865

# Observation Result:
# - The fluctuation of average relative humidity has small impact to soil moisture. Maybe, it's due to partial condensation
#   phenomenon, so when the precipitation probability is relatively stable (almost flat), the soil moisture is still fluctuating.
#   This phenomenon is used in Fog Farming to supply fresh water in arid/semi-arid area by using a lot of nets to help water vapour condensation.

# Find Daily Maximum of Precipitation Probability (Table)
dfdmax1 = df1.groupby('ymd')[df1h.columns[0:]].max().sort_values('precipitation_probability', ascending=False).head(5)
dfdmax1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
ymd
2025-05-05	70.0	100.0	0.264	0.135	93.0	14.243151	1.271882	20.6695	99.0	100.0	16.923830	0.138	17.960001	24.264000
2025-05-06	54.0	100.0	0.245	0.206	96.0	14.011498	1.296059	21.6695	98.0	100.0	18.971008	0.161	22.160000	27.664000
2025-05-04	47.0	100.0	0.181	0.072	76.0	11.103669	2.930076	27.6695	99.0	100.0	27.776047	0.112	26.560000	31.664000
2025-04-02	23.0	100.0	0.117	0.060	59.0	2.182480	1.845239	18.4195	0.0	100.0	37.664326	0.094	15.560000	25.864000
2025-05-03	20.0	99.0	0.042	0.051	24.0	5.358303	4.346065	32.2695	100.0	0.0	41.180286	0.085	29.610000	41.463997

# Find Daily Maximum of Precipitation Probability (Plot)
dfdmax2 = df1.groupby('ymd')[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].max().sort_values('ymd', ascending=True)
dfdmax2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='ymd'>, <Axes: xlabel='ymd'>], dtype=object)

# Find Daily Maximum of Relative Humidity 2m (Table)
dfdmax2 = df1.groupby('ymd')[df1h.columns[[4,0]]].max().sort_values('relative_humidity_2m', ascending=False).head(10)
dfdmax2

	relative_humidity_2m	precipitation_probability
ymd
2025-05-06	96.0	54.0
2025-05-05	93.0	70.0
2025-05-04	76.0	47.0
2025-05-07	69.0	10.0
2025-04-03	63.0	11.0
2025-05-08	59.0	0.0
2025-04-02	59.0	23.0
2025-05-18	55.0	20.0
2025-04-27	45.0	4.0
2025-03-31	43.0	0.0

# Find Monthly Maximum values of Precipitation Probability (Table)
dfmmax1 = df1.groupby(['year','month'])[df1h.columns[0:]].max().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmmax1

		precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
year	month
2025	05	70.0	100.0	0.264	0.245	96.0	14.243151	7.356871	41.3195	100.0	100.0	47.619442	0.194	38.010000	51.213997
	04	23.0	100.0	0.121	0.060	63.0	3.911096	5.367447	35.4695	100.0	100.0	40.352130	0.095	33.359997	44.814000
	03	3.0	100.0	0.047	0.069	43.0	4.301982	4.619241	32.7695	100.0	5.0	45.504055	0.101	30.310000	41.064000
	06	3.0	100.0	0.032	0.078	15.0	4.657262	7.146035	40.7695	100.0	0.0	29.810522	0.107	38.309998	45.614000

# Find Monthly Maximum values of Precipitation Probability (Plot)
dfmmax2 = df1.groupby(['year','month'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].max().sort_values(by = ['year','month'], ascending=[True,True])
dfmmax2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='year,month'>, <Axes: xlabel='year,month'>],
      dtype=object)

# Find Monthly Maximum values of Relative Humidity 2m (Table)
dfmmax2 = df1.groupby(['year','month'])[df1h.columns[[4,0]]].max().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfmmax2

		relative_humidity_2m	precipitation_probability
year	month
2025	05	96.0	70.0
	04	63.0	23.0
	03	43.0	3.0
	06	15.0	3.0

# Find Hourly Maximum values of Precipitation Probability (Table)
dfhmax1 = df1.groupby(['hmstz'])[df1h.columns[0:]].max().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfhmax1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
hmstz
23:00:00+00:00	70.0	100.0	0.264	0.196	93.0	14.243151	7.012977	40.619500	100.0	98.0	44.554268	0.193	38.010000	47.564000
06:00:00+00:00	54.0	100.0	0.257	0.232	91.0	13.314399	5.457564	36.169502	100.0	67.0	29.723444	0.192	33.960000	31.814001
05:00:00+00:00	53.0	100.0	0.263	0.220	91.0	12.968334	5.441759	35.719500	100.0	100.0	47.165497	0.192	35.260000	30.914000
22:00:00+00:00	51.0	100.0	0.184	0.199	70.0	12.657084	7.356871	41.319500	100.0	56.0	43.565945	0.193	37.809998	50.064000
16:00:00+00:00	47.0	100.0	0.219	0.222	85.0	13.694188	5.022630	35.269500	100.0	100.0	36.086296	0.193	31.360000	38.163998
00:00:00+00:00	46.0	100.0	0.238	0.195	83.0	12.350466	7.146035	40.769500	100.0	99.0	42.682400	0.194	38.160000	45.364000
15:00:00+00:00	46.0	100.0	0.224	0.225	94.0	14.011498	4.482165	33.219500	100.0	100.0	37.017033	0.193	30.260000	33.913998
17:00:00+00:00	44.0	100.0	0.210	0.219	76.0	13.183273	5.467945	36.819500	100.0	100.0	36.304707	0.194	33.010000	43.014000
18:00:00+00:00	41.0	100.0	0.199	0.215	65.0	12.128211	6.063636	38.519500	100.0	44.0	41.945793	0.194	34.609997	46.413998
19:00:00+00:00	38.0	100.0	0.185	0.211	55.0	10.796674	6.279035	39.169502	100.0	96.0	47.619442	0.194	35.760000	49.864000
11:00:00+00:00	37.0	100.0	0.233	0.233	93.0	13.400532	3.909449	30.869501	100.0	100.0	39.990795	0.190	30.110000	29.164000
04:00:00+00:00	37.0	100.0	0.232	0.200	90.0	13.095613	5.456728	35.769500	100.0	100.0	39.246605	0.193	36.010000	32.264000
12:00:00+00:00	35.0	100.0	0.233	0.232	91.0	13.413273	3.844822	30.019500	100.0	100.0	40.063644	0.190	29.410000	28.414000
14:00:00+00:00	35.0	100.0	0.228	0.227	94.0	13.713717	4.152573	32.269500	100.0	100.0	34.942265	0.192	28.960001	30.814001
10:00:00+00:00	34.0	100.0	0.237	0.236	94.0	13.614459	3.945178	31.069500	100.0	100.0	40.609850	0.191	30.710001	30.014000
07:00:00+00:00	33.0	100.0	0.262	0.245	94.0	13.664090	4.659209	34.119500	100.0	90.0	37.971040	0.192	33.059998	30.864000
03:00:00+00:00	32.0	100.0	0.224	0.196	88.0	12.948845	6.151083	37.769500	100.0	100.0	46.938255	0.193	38.059998	34.614000
01:00:00+00:00	32.0	100.0	0.205	0.192	87.0	13.019988	7.014605	40.419502	100.0	93.0	38.813274	0.194	38.309998	42.163998
21:00:00+00:00	27.0	100.0	0.182	0.203	59.0	11.153346	6.922008	40.169502	100.0	100.0	44.653060	0.193	37.410000	50.514000
08:00:00+00:00	26.0	100.0	0.251	0.245	93.0	13.549228	4.443540	33.269500	100.0	36.0	43.476612	0.191	32.660000	30.864000
02:00:00+00:00	25.0	100.0	0.245	0.190	87.0	12.970818	6.830472	39.919502	100.0	96.0	41.531270	0.193	38.309998	38.114000
13:00:00+00:00	23.0	100.0	0.231	0.230	96.0	13.739599	3.685421	30.269500	100.0	100.0	45.504055	0.191	29.360000	28.314001
20:00:00+00:00	21.0	100.0	0.174	0.207	51.0	10.173055	6.597355	39.269500	100.0	100.0	44.606598	0.194	36.609997	51.213997
09:00:00+00:00	14.0	100.0	0.242	0.241	94.0	13.664090	4.237368	32.219500	100.0	100.0	38.479603	0.191	31.160000	30.764000

# Find Hourly Maximum values of Precipitation Probability (Plot)
dfhmax2 = df1.groupby(['hmstz'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].max().sort_values(by = ['hmstz'], ascending=[True])
dfhmax2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='hmstz'>, <Axes: xlabel='hmstz'>], dtype=object)

# Find Hourly Maximum values of Relative Humidity 2m (Table)
dfhmax2 = df1.groupby(['hmstz'])[df1h.columns[[4,0]]].max().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfhmax2

	relative_humidity_2m	precipitation_probability
hmstz
13:00:00+00:00	96.0	23.0
09:00:00+00:00	94.0	14.0
15:00:00+00:00	94.0	46.0
14:00:00+00:00	94.0	35.0
10:00:00+00:00	94.0	34.0
07:00:00+00:00	94.0	33.0
23:00:00+00:00	93.0	70.0
11:00:00+00:00	93.0	37.0
08:00:00+00:00	93.0	26.0
12:00:00+00:00	91.0	35.0
06:00:00+00:00	91.0	54.0
05:00:00+00:00	91.0	53.0
04:00:00+00:00	90.0	37.0
03:00:00+00:00	88.0	32.0
01:00:00+00:00	87.0	32.0
02:00:00+00:00	87.0	25.0
16:00:00+00:00	85.0	47.0
00:00:00+00:00	83.0	46.0
17:00:00+00:00	76.0	44.0
22:00:00+00:00	70.0	51.0
18:00:00+00:00	65.0	41.0
21:00:00+00:00	59.0	27.0
19:00:00+00:00	55.0	38.0
20:00:00+00:00	51.0	21.0

# Observation Result:
# - When the maximum value of 'relative_humidity_2m' and 'precipitation_probability' decreases, the maximum value of moisture rises. Maybe, it's due to
#   the occurence of rain (humidity falls due to part of water vapour turns to liquid, and after that, the precipitation_probability falls also because
#   it's already release the water vapour as rain).

# Find Daily Minimum of Precipitation Probability (Table)
dfdmin1 = df1.groupby('ymd')[df1h.columns[0:]].min().sort_values(['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm'], ascending=True).head(5)
dfdmin1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
ymd
2025-03-20	0.0	-1.0	0.037	0.067	12.0	-14.492778	1.159523	11.559500	0.0	0.0	2.160000	0.099	11.060000	5.064
2025-04-10	0.0	0.0	0.030	0.041	5.0	-12.953846	1.588309	17.269500	0.0	0.0	3.758510	0.089	22.960001	16.014
2025-04-11	0.0	0.0	0.030	0.042	5.0	-16.909462	1.542835	16.519500	0.0	0.0	3.240000	0.088	23.060000	16.964
2025-04-12	0.0	0.0	0.030	0.043	6.0	-11.360874	2.328790	22.769500	0.0	0.0	7.491114	0.087	21.710001	18.164
2025-04-13	0.0	0.0	0.030	0.043	6.0	-12.012876	1.915399	20.369501	0.0	0.0	5.447788	0.087	19.810000	15.514

# Find Daily Minimum of Precipitation Probability (Plot)
dfdmin2 = df1.groupby('ymd')[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].min().sort_values('ymd', ascending=True)
dfdmin2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='ymd'>, <Axes: xlabel='ymd'>], dtype=object)

# Find Daily Minimum of relative_humidity_2m (Table)
dfdmin3 = df1.groupby('ymd')[df1h.columns[[4,0]]].min().sort_values(['relative_humidity_2m'], ascending=True).head(5)
dfdmin3

	relative_humidity_2m	precipitation_probability
ymd
2025-05-22	4.0	0.0
2025-05-23	4.0	0.0
2025-05-24	4.0	0.0
2025-04-25	5.0	0.0
2025-04-07	5.0	0.0

# Observation Result of Daily Minimum Data
# - Fluctuation of 'relative_humidity_2m' is similar to 'soil_moisture_0_to_1cm', different compared to Hourly Minimum Data, 
#   maybe due to longer time scope.

# Find Monthly Minimum values of Precipitation Probability (Table)
dfmmin1 = df1.groupby(['year','month'])[df1h.columns[0:]].min().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[True,True,True,True,True,True,True,True,True,True])
dfmmin1

		precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
year	month
2025	03	0.0	-1.0	0.031	0.060	7.0	-14.492778	0.848216	9.1595	0.0	0.0	0.509117	0.095	8.060000	3.214000
	04	0.0	0.0	0.030	0.033	5.0	-17.648800	0.364434	6.4195	0.0	0.0	0.509117	0.085	7.310000	6.114000
	05	0.0	0.0	0.030	0.049	4.0	-14.610750	0.065552	13.2195	0.0	0.0	0.720000	0.084	12.960000	13.114000
	06	0.0	0.0	0.031	0.076	6.0	-5.956471	3.654214	30.0195	0.0	0.0	9.000000	0.105	28.960001	28.314001

# Find Monthly Minimum values of Precipitation Probability (Plot)
dfmmin2 = df1.groupby(['year','month'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].min().sort_values(by = ['year','month'], ascending=[True,True])
dfmmin2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='year,month'>, <Axes: xlabel='year,month'>],
      dtype=object)

# Find Monthly Minimum values of Relative Humidity 2m (Table)
dfmmin2 = df1.groupby(['year','month'])[df1h.columns[[4,0]]].min().sort_values(by = ['relative_humidity_2m'], ascending=[True])
dfmmin2

		relative_humidity_2m	precipitation_probability
year	month
2025	05	4.0	0.0
	04	5.0	0.0
	06	6.0	0.0
	03	7.0	0.0

# Find Hourly Minimum values of Precipitation Probability (Table)
dfhmin1 = df1.groupby(['hmstz'])[df1h.columns[0:]].min().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[True,True,True,True,True,True,True,True,True,True])
dfhmin1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
hmstz
12:00:00+00:00	0.0	-1.0	0.031	0.034	8.0	-14.438404	0.152326	7.419500	0.0	0.0	2.160000	0.085	7.510000	4.314000
01:00:00+00:00	0.0	0.0	0.030	0.033	4.0	-17.574831	0.224315	14.169499	0.0	0.0	0.509117	0.085	9.610001	16.414000
02:00:00+00:00	0.0	0.0	0.030	0.033	4.0	-16.503166	0.223596	13.869500	0.0	0.0	2.620839	0.085	10.560000	12.514000
00:00:00+00:00	0.0	0.0	0.030	0.033	4.0	-14.659351	0.294288	15.169499	0.0	0.0	2.545584	0.085	9.960000	17.164000
03:00:00+00:00	0.0	0.0	0.030	0.033	5.0	-17.648800	0.203762	12.619500	0.0	0.0	1.484318	0.085	11.010000	10.714001
04:00:00+00:00	0.0	0.0	0.030	0.033	5.0	-16.758446	0.167628	12.369500	0.0	0.0	1.138420	0.085	11.110001	9.614000
05:00:00+00:00	0.0	0.0	0.030	0.033	6.0	-14.917891	0.147976	11.419499	0.0	0.0	2.414954	0.085	10.560000	9.264000
06:00:00+00:00	0.0	0.0	0.030	0.033	8.0	-14.170350	0.151350	10.669499	0.0	0.0	2.620839	0.085	9.260000	8.014000
23:00:00+00:00	0.0	0.0	0.030	0.034	4.0	-14.929487	0.122335	14.959500	0.0	0.0	2.968636	0.084	10.810000	17.564001
22:00:00+00:00	0.0	0.0	0.030	0.034	4.0	-13.845597	0.628145	14.559500	0.0	0.0	2.189795	0.084	10.960000	22.314001
21:00:00+00:00	0.0	0.0	0.030	0.034	5.0	-14.218192	0.903590	13.319500	0.0	0.0	1.938659	0.084	10.310000	22.714000
08:00:00+00:00	0.0	0.0	0.031	0.033	5.0	-16.909462	0.116959	8.619500	0.0	0.0	1.297998	0.085	8.110000	6.914000
07:00:00+00:00	0.0	0.0	0.031	0.033	6.0	-14.856331	0.099928	9.719500	0.0	0.0	1.297998	0.085	8.610001	7.914000
09:00:00+00:00	0.0	0.0	0.031	0.033	7.0	-14.147417	0.099928	8.119500	0.0	0.0	2.741678	0.085	7.860000	5.864000
11:00:00+00:00	0.0	0.0	0.031	0.033	8.0	-14.150644	0.115834	7.369500	0.0	0.0	1.609969	0.085	7.610000	5.214000
10:00:00+00:00	0.0	0.0	0.031	0.033	9.0	-14.670770	0.099606	8.019500	0.0	0.0	1.440000	0.085	7.760000	5.314000
20:00:00+00:00	0.0	0.0	0.031	0.034	5.0	-14.367883	0.694066	11.819500	0.0	0.0	0.804984	0.084	9.860001	22.814001
19:00:00+00:00	0.0	0.0	0.031	0.034	6.0	-13.942364	0.703295	11.719500	0.0	0.0	2.160000	0.084	10.160000	23.464000
18:00:00+00:00	0.0	0.0	0.031	0.034	8.0	-14.892031	0.661922	11.719500	0.0	0.0	1.835647	0.084	10.360001	20.364000
17:00:00+00:00	0.0	0.0	0.031	0.034	9.0	-14.362545	0.448559	10.719500	0.0	0.0	0.509117	0.084	9.660000	18.064001
15:00:00+00:00	0.0	0.0	0.031	0.034	10.0	-13.997224	0.102202	8.869500	0.0	0.0	1.138420	0.084	7.660000	7.414000
16:00:00+00:00	0.0	0.0	0.031	0.034	10.0	-13.957830	0.276782	9.269500	0.0	0.0	0.720000	0.084	8.860001	13.864000
14:00:00+00:00	0.0	0.0	0.031	0.034	11.0	-16.555761	0.100250	6.719500	0.0	0.0	0.720000	0.085	7.310000	3.214000
13:00:00+00:00	0.0	0.0	0.031	0.034	11.0	-15.873536	0.065552	6.419500	0.0	0.0	1.484318	0.085	7.410000	3.664000

# Find Hourly Minimum values of Precipitation Probability (Plot)
dfhmin2 = df1.groupby(['hmstz'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].min().sort_values(by = ['hmstz'], ascending=[True])
dfhmin2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='hmstz'>, <Axes: xlabel='hmstz'>], dtype=object)

# Find Hourly Minimum values of Relative Humidity 2m (Table)
dfhmin2 = df1.groupby(['hmstz'])[df1h.columns[[4,0]]].min().sort_values(by = ['relative_humidity_2m'], ascending=[True])
dfhmin2

	relative_humidity_2m	precipitation_probability
hmstz
00:00:00+00:00	4.0	0.0
22:00:00+00:00	4.0	0.0
23:00:00+00:00	4.0	0.0
01:00:00+00:00	4.0	0.0
02:00:00+00:00	4.0	0.0
03:00:00+00:00	5.0	0.0
04:00:00+00:00	5.0	0.0
21:00:00+00:00	5.0	0.0
08:00:00+00:00	5.0	0.0
20:00:00+00:00	5.0	0.0
05:00:00+00:00	6.0	0.0
07:00:00+00:00	6.0	0.0
19:00:00+00:00	6.0	0.0
09:00:00+00:00	7.0	0.0
18:00:00+00:00	8.0	0.0
11:00:00+00:00	8.0	0.0
12:00:00+00:00	8.0	0.0
06:00:00+00:00	8.0	0.0
17:00:00+00:00	9.0	0.0
10:00:00+00:00	9.0	0.0
16:00:00+00:00	10.0	0.0
15:00:00+00:00	10.0	0.0
14:00:00+00:00	11.0	0.0
13:00:00+00:00	11.0	0.0

# Observation result:
# - When the minimum value of 'relative_humidity_2m' rises, the min value of 'soil_moisture_0_to_1cm' doesn't immediately rise, but instead, steadily almost flat. Maybe, it's due to part of liquid water in the soil vaporize and change into water vapour, so increase the relative humidity of air (near the soil).
# - When 'relative_humidity_2m' falls, the value of 'soil_moisture_0_to_1cm' doesn't immediately falls, but instead, in contrary, rises big enough. Maybe, it's due to part of water vapour in the air near the soil condensates and add liquid water in the soil so the soil moisture increases.

Mode value of a Column (for sample, we take 'temperature_2m' Column)

# Find Mode value if not rounded
dfmod = df1.mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod = dfmod.dropna()
dfmod
# The result is 17.4525 if we don't do any number rounding. But this result in the most cases, maybe is not the best mode at all.

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	437	2025-03-19 05:00:00+00:00	20.8195	9.0	-11.41719	1.279143	0.0	0.0	0.0	0.0	1007.1	937.6389	0.0	0.0	0.0	0.0	90000.0	7.42159	0.031	0.051	0.087	0.1	0.105	0.0	0.0	0.0	0.0	1.754564	3.319036	4.42464	4.024922	135.0001	180.0	180.0	135.0001	26.01	26.41	20.864	20.364	20.814001	23.414	25.114	10.440001	Las Vegas	2025-03-20	05:00:00+00:00	2025	05	01	05	00	00+00	00

dfmoda = df1[(df1['temperature_2m'] >= 17.45245) &  (df1['temperature_2m'] < 17.45255)] #.where(df1['temperature_2m'] == 19)
dfmoda = dfmoda.dropna()
print(dfmoda['temperature_2m'])
len(dfmoda)
# The result: the temperature of 17.4525 only occurred 11 times out of 1651 data samples.

Series([], Name: temperature_2m, dtype: float64)

0

# Find Mode value precise to 0 decimal number
dfmod0 = df1.round(0).mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod0 = dfmod0.dropna()
dfmod0
# The result is 19 if we round it to the nearest integer (0 decimal number)

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	437	2025-03-19 05:00:00+00:00	22.0	9.0	-3.0	19.0	0.0	0.0	0.0	0.0	1008.0	940.0	0.0	0.0	0.0	0.0	90000.0	7.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	7.0	6.0	7.0	135.0	180.0	180.0	223.0	27.0	26.0	25.0	18.0	22.0	23.0	23.0	10.0	Las Vegas	2025-03-20	05:00:00+00:00	2025	05	01	05	00	00+00	00

# Find out how many occurrences of 'temperature_2m' rounded to 19C (0 decimal number)
dfmod0a = df1[(df1['temperature_2m'] >= 18.5) &  (df1['temperature_2m'] < 19.5)]
dfmod0a = dfmod0a.dropna()
print(dfmod0a['temperature_2m'])
# The result: temperature of 19C occurred 127 times out of 1651 data samples.
# This is far more occurences than if we use the 17.4525C (4 decimal numbers)

480     18.959500
481     19.259500
482     19.359500
483     19.259500
484     18.559500
          ...    
1835    19.019500
1887    19.069500
1907    19.069500
1931    19.219501
1932    19.019500
Name: temperature_2m, Length: 82, dtype: float64

# Find Mode value of Precipitation Probability (Table) precise to 1 decimal number
dfmod1 = df1.round(1).mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod1 = dfmod1.dropna()
dfmod1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	437	2025-03-19 05:00:00+00:00	23.4	9.0	-2.3	20.5	0.0	0.0	0.0	0.0	1007.1	939.7	0.0	0.0	0.0	0.0	90000.0	7.6	0.0	0.1	0.1	0.1	0.1	0.0	0.0	0.0	0.1	1.5	7.6	6.3	1.8	135.0	180.0	180.0	135.0	22.3	17.2	20.9	20.4	22.6	23.4	25.1	10.4	Las Vegas	2025-03-20	05:00:00+00:00	2025	05	01	05	00	00+00	00

# Find out how many occurrences of 'temperature_2m' rounded to 19.7C (1 decimal number)
dfmod1a = df1[(df1['temperature_2m'] >= 19.65) &  (df1['temperature_2m'] < 19.75)] #.where(df1['temperature_2m'] == 19)
dfmod1a = dfmod1a.dropna()
print(dfmod1a['temperature_2m'])
len(dfmod1a)
# The result: the temperature of 19.7C occurred only 18 times out of 1651 data samples.

564     19.659500
639     19.719501
657     19.669500
919     19.719501
920     19.719501
1285    19.719501
1390    19.669500
1555    19.669500
1560    19.719501
1834    19.719501
Name: temperature_2m, dtype: float64

10

# Find Mode value of Precipitation Probability (Table) precise to 2 decimal numbers
dfmod2 = df1.round(2).mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod2 = dfmod2.dropna()
dfmod2

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	437	2025-03-19 05:00:00+00:00	20.82	9.0	-1.7	14.61	0.0	0.0	0.0	0.0	1007.1	941.44	0.0	0.0	0.0	0.0	90000.0	7.42	0.03	0.05	0.09	0.1	0.1	0.0	0.0	0.0	0.12	1.93	3.32	7.39	4.02	135.0	180.0	180.0	135.0	26.01	26.41	20.86	20.36	20.81	23.41	25.11	10.44	Las Vegas	2025-03-20	05:00:00+00:00	2025	05	01	05	00	00+00	00

# Find out how many occurrences of 'temperature_2m' rounded to 17.45C (2 decimal numbers)
dfmod2a = df1[(df1['temperature_2m'] >= 17.445) &  (df1['temperature_2m'] < 17.455)] #.where(df1['temperature_2m'] == 19)
dfmod2a = dfmod2a.dropna()
print(dfmod2a['temperature_2m'])
len(dfmod2a)
# The result: the temperature of 17.45C occurred only 11 times out of 1651 data samples. This is perfectly the same as if we don't round it.

Series([], Name: temperature_2m, dtype: float64)

0

# CONCLUSION: to get the nearest possible Mode value of a Column (in this sample, we take 'temperature_2m'), we can't just take the real values
# but, instead, maybe we have to round it to the nearest integer/whole number.

Standard Deviation and Outliers of Precipitation Probability

# Function Definition
def find_outliers_iqr(series):
       Q1 = series.quantile(0.25)
       print('Q1 = ', Q1)
       Q2 = series.quantile(0.50)
       print('Q2 = ', Q2)
       Q3 = series.quantile(0.75)
       print('Q3 = ', Q3)
       IQR = Q3 - Q1
       lower_bound = Q1 - 1.5 * IQR
       print('Lower Bound = ', lower_bound)
       upper_bound = Q3 + 1.5 * IQR
       print('Upper Bound = ', upper_bound)
       outliers = series[(series < lower_bound) | (series > upper_bound)]
       return outliers

##### Standard Deviation of 'precipitation_probability'
df1stdevpp = df1[['precipitation_probability']].std() #.sort_values(ascending=False)
df1stdevpp

precipitation_probability    5.284249
dtype: float64

df1[['precipitation_probability']].mean()

precipitation_probability    1.236769
dtype: float64

# Distribution Plot of 'precipitation_probability'
df1[['precipitation_probability']].plot.kde()

<Axes: ylabel='Density'>

##### Outliers of 'precipitation_probability'
# Quantiles of 'precipitation_probability'
# Note: either Lower Bound and/or Upper Bound doesn't have to be exist at all in the Dataset.
find_outliers_iqr(df1[['precipitation_probability']]).dropna().sort_values(by='precipitation_probability', ascending=False)

Q1 =  precipitation_probability    0.0
Name: 0.25, dtype: float64
Q2 =  precipitation_probability    0.0
Name: 0.5, dtype: float64
Q3 =  precipitation_probability    0.0
Name: 0.75, dtype: float64
Lower Bound =  precipitation_probability    0.0
dtype: float64
Upper Bound =  precipitation_probability    0.0
dtype: float64

	precipitation_probability
1583	70.0
1590	54.0
1589	53.0
1582	51.0
1552	47.0
...	...
1176	1.0
1175	1.0
1174	1.0
1173	1.0
524	1.0

342 rows × 1 columns

# Box Plot of 'precipitation_probability'
# Note:
# Top black horizontal line is Upper Bound value
# Bottom black horizontal line is Lower Bound value
# Lower blue line is 25 percentile
# Green line is Median a.k.a 50 percentile
# Upper blue line is 75 percentile
df1[['precipitation_probability']].boxplot()

<Axes: >

# Draw the Scatter Plot of 'precipitation_probability'
# We can see that the dots are concentrated at below/bottom (below the Upper Bound of 17.5)
import matplotlib.pyplot as plt
df1[['date','precipitation_probability']].plot.scatter(x='date', y='precipitation_probability', s=1, c='green')

<Axes: xlabel='date', ylabel='precipitation_probability'>

##### Standard Deviation of 'temperature_2m'
df1stdevtemp = df1[['temperature_2m']].std() #.sort_values(ascending=False)
df1stdevtemp

temperature_2m    6.807911
dtype: float64

df1[['temperature_2m']].mean()

temperature_2m    23.069952
dtype: float64

df1[['temperature_2m']].max()

temperature_2m    41.3195
dtype: float64

df1[['temperature_2m']].plot.kde()

<Axes: ylabel='Density'>

##### Outliers of 'temperature_2m'
# Quantiles of 'temperature_2m'
# Note: either Lower Bound and/or Upper Bound doesn't have to be exist at all in the Dataset.
outlierstemp = find_outliers_iqr(df1[['temperature_2m']])
print(outlierstemp.dropna().sort_values(by='temperature_2m', ascending=False))

Q1 =  temperature_2m    17.7695
Name: 0.25, dtype: float64
Q2 =  temperature_2m    22.7695
Name: 0.5, dtype: float64
Q3 =  temperature_2m    27.844501
Name: 0.75, dtype: float64
Lower Bound =  temperature_2m    2.656999
dtype: float64
Upper Bound =  temperature_2m    42.957001
dtype: float64
Empty DataFrame
Columns: [temperature_2m]
Index: []

# Box Plot of 'temperature_2m'
# Note:
# Top black horizontal line is Upper Bound value
# Bottom black horizontal line is Lower Bound value
# Lower blue line is 25 percentile
# Green line is Median a.k.a 50 percentile
# Upper blue line is 75 percentile
df1[['temperature_2m']].boxplot()

<Axes: >

# Draw the Scatter Plot of 'temperature_2m'
# We can see that the dots are concentrated at below/bottom (below the Upper Bound of 17.5)
import matplotlib.pyplot as plt
df1[['date','temperature_2m']].plot.scatter(x='date', y='temperature_2m', s=1, c='green')

<Axes: xlabel='date', ylabel='temperature_2m'>

# Boxplot of all Columns
df1[df1.columns[1:9]].boxplot()

<Axes: >

df1[df1.columns[9:17]].boxplot()

<Axes: >

df1[df1.columns[17:26]].boxplot()

<Axes: >

df1[df1.columns[26:35]].boxplot()

<Axes: >

df1[df1.columns[35:]].boxplot()

<Axes: >